wip

.clang-format

@@ -22,14 +22,7 @@ AllowShortIfStatementsOnASingleLine: Never
 AllowShortLambdasOnASingleLine: Inline
 AllowShortLoopsOnASingleLine: false
 AlwaysBreakBeforeMultilineStrings: true
-# Treat CUDA keywords/attributes as "attribute macros" and avoid breaking lines inside them
-AttributeMacros:
-  - __host__
-  - __device__
-  - __global__
-  - __forceinline__
-  - __launch_bounds__
-BinPackArguments: true
+BinPackArguments: false
 BinPackParameters: false # OnePerLine
 BitFieldColonSpacing: Both
 BreakBeforeBraces: Custom # Attach

.devops/rocm.Dockerfile

@@ -4,7 +4,7 @@ ARG UBUNTU_VERSION=24.04
 ARG ROCM_VERSION=6.4
 ARG AMDGPU_VERSION=6.4
 
-# Target the ROCm build image
+# Target the CUDA build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
 
 ### Build image
@@ -15,13 +15,16 @@ FROM ${BASE_ROCM_DEV_CONTAINER} AS build
 # This is mostly tied to rocBLAS supported archs.
 # gfx803, gfx900, gfx1032, gfx1101, gfx1102,not officialy supported
 # gfx906 is deprecated
-#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.1/reference/system-requirements.html
+#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.2.4/reference/system-requirements.html
 
-ARG ROCM_DOCKER_ARCH='gfx803;gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1010;gfx1030;gfx1032;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx1151'
-#ARG ROCM_DOCKER_ARCH='gfx1151'
+ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102'
+#ARG ROCM_DOCKER_ARCH=gfx1100
 
-# Set ROCm architectures
+# Set nvcc architectured
 ENV AMDGPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+# ENV CC=/opt/rocm/llvm/bin/clang
+# ENV CXX=/opt/rocm/llvm/bin/clang++
 
 RUN apt-get update \
     && apt-get install -y \
@@ -36,16 +39,8 @@ WORKDIR /app
 
 COPY . .
 
-RUN git clone https://github.com/rocm/rocwmma --branch develop --depth 1
-
 RUN HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
-    cmake -S . -B build \
-        -DGGML_HIP=ON \
-        -DGGML_HIP_ROCWMMA_FATTN=ON \
-        -DCMAKE_HIP_FLAGS="-I$(pwd)/rocwmma/library/include/" \
-        -DAMDGPU_TARGETS="$ROCM_DOCKER_ARCH" \
-        -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON \
-        -DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF \
+    cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF \
     && cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib \

.editorconfig

@@ -52,11 +52,3 @@ insert_final_newline = unset
 [vendor/miniaudio/miniaudio.h]
 trim_trailing_whitespace = unset
 insert_final_newline = unset
-
-[tools/server/webui/**]
-indent_style = unset
-indent_size = unset
-end_of_line = unset
-charset = unset
-trim_trailing_whitespace = unset
-insert_final_newline = unset

.github/workflows/build.yml

@@ -56,7 +56,7 @@ env:
 
 jobs:
   macOS-latest-cmake-arm64:
-    runs-on: macos-latest
+    runs-on: macos-14
 
     steps:
       - name: Clone
@@ -88,7 +88,6 @@ jobs:
             -DGGML_METAL_SHADER_DEBUG=ON \
             -DGGML_RPC=ON
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
-          leaks -atExit -- ./build/bin/test-thread-safety -hf ggml-org/gemma-3-270m-qat-GGUF -ngl 99 -p "$(printf 'hello %.0s' {1..128})" -n 16 -c 512 -ub 32 -np 2 -t 2 -lv 1
 
       - name: Test
         id: cmake_test
@@ -127,8 +126,7 @@ jobs:
             -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DGGML_METAL=OFF \
-            -DGGML_RPC=ON \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
+            -DGGML_RPC=ON
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
@@ -138,7 +136,7 @@ jobs:
           ctest -L main --verbose --timeout 900
 
   macOS-latest-cmake-arm64-webgpu:
-    runs-on: macos-latest
+    runs-on: macos-14
 
     steps:
       - name: Clone
@@ -711,7 +709,6 @@ jobs:
 
   macOS-latest-swift:
     runs-on: macos-latest
-    needs: ios-xcode-build
 
     strategy:
       matrix:
@@ -728,12 +725,6 @@ jobs:
           key: macOS-latest-swift
           evict-old-files: 1d
 
-      - name: Download xcframework artifact
-        uses: actions/download-artifact@v4
-        with:
-          name: llama-xcframework
-          path: build-apple/llama.xcframework/
-
       - name: Dependencies
         id: depends
         continue-on-error: true
@@ -755,6 +746,11 @@ jobs:
             -DCMAKE_OSX_ARCHITECTURES="arm64;x86_64"
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 
+      - name: xcodebuild for swift package
+        id: xcodebuild
+        run: |
+          ./build-xcframework.sh
+
   windows-msys2:
     runs-on: windows-2025
 
@@ -1054,13 +1050,9 @@ jobs:
         run:  examples/sycl/win-build-sycl.bat
 
   windows-latest-cmake-hip:
+    if: ${{ github.event.inputs.create_release != 'true' }}
     runs-on: windows-2022
 
-    env:
-      # The ROCm version must correspond to the version used in the HIP SDK.
-      ROCM_VERSION: "6.4.2"
-      HIPSDK_INSTALLER_VERSION: "25.Q3"
-
     steps:
       - name: Clone
         id: checkout
@@ -1069,46 +1061,23 @@ jobs:
       - name: Clone rocWMMA repository
         id: clone_rocwmma
         run: |
-          git clone https://github.com/rocm/rocwmma --branch rocm-${{ env.ROCM_VERSION }} --depth 1
+          git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1
 
-      - name: Cache ROCm Installation
-        id: cache-rocm
-        uses: actions/cache@v4
-        with:
-          path: C:\Program Files\AMD\ROCm
-          key: rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
-
-      - name: Install ROCm
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
+      - name: Install
         id: depends
         run: |
           $ErrorActionPreference = "Stop"
           write-host "Downloading AMD HIP SDK Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP SDK"
           $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
-          $completed = $proc.WaitForExit(600000)
-          if (-not $completed) {
-              Write-Error "ROCm installation timed out after 10 minutes. Killing the process"
-              $proc.Kill()
-              exit 1
-          }
-          if ($proc.ExitCode -ne 0) {
-              Write-Error "ROCm installation failed with exit code $($proc.ExitCode)"
-              exit 1
-          }
+          $proc.WaitForExit(600000)
           write-host "Completed AMD HIP SDK installation"
 
       - name: Verify ROCm
         id: verify
         run: |
-          # Find and test ROCm installation
-          $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1
-          if (-not $clangPath) {
-            Write-Error "ROCm installation not found"
-            exit 1
-          }
-          & $clangPath.FullName --version
+          & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
 
       - name: Install ccache
         uses: ggml-org/ccache-action@v1.2.16
@@ -1172,17 +1141,8 @@ jobs:
         run: |
           ./build-xcframework.sh
 
-      - name: Upload xcframework artifact
-        uses: actions/upload-artifact@v4
-        with:
-          name: llama-xcframework
-          path: build-apple/llama.xcframework/
-          retention-days: 1
-
       - name: Build Xcode project
-        run: |
-          xcodebuild -downloadPlatform iOS
-          xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build
+        run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' FRAMEWORK_FOLDER_PATH=./build-ios build
 
   android-build:
     runs-on: ubuntu-latest

.github/workflows/close-issue.yml

@@ -17,7 +17,7 @@ jobs:
     steps:
       - uses: actions/stale@v5
         with:
-          exempt-issue-labels: "refactoring,help wanted,good first issue,research 🔬,bug,roadmap"
+          exempt-issue-labels: "refactoring,help wanted,good first issue,research,bug,roadmap"
           days-before-issue-stale: 30
           days-before-issue-close: 14
           stale-issue-label: "stale"

.github/workflows/release.yml

@@ -108,8 +108,7 @@ jobs:
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DGGML_METAL=OFF \
-            -DGGML_RPC=ON \
-            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
+            -DGGML_RPC=ON
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Determine tag name
@@ -529,14 +528,11 @@ jobs:
   windows-hip:
     runs-on: windows-2022
 
-    env:
-      HIPSDK_INSTALLER_VERSION: "25.Q3"
-
     strategy:
       matrix:
         include:
           - name: "radeon"
-            gpu_targets: "gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
+            gpu_targets: "gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
 
     steps:
       - name: Clone
@@ -546,52 +542,29 @@ jobs:
       - name: Clone rocWMMA repository
         id: clone_rocwmma
         run: |
-          git clone https://github.com/rocm/rocwmma --branch develop --depth 1
-
-      - name: Cache ROCm Installation
-        id: cache-rocm
-        uses: actions/cache@v4
-        with:
-          path: C:\Program Files\AMD\ROCm
-          key: rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
+          git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: windows-latest-cmake-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}-x64
+          key: windows-latest-cmake-hip-${{ matrix.name }}-x64
           evict-old-files: 1d
 
-      - name: Install ROCm
-        if: steps.cache-rocm.outputs.cache-hit != 'true'
+      - name: Install
         id: depends
         run: |
           $ErrorActionPreference = "Stop"
           write-host "Downloading AMD HIP SDK Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
           write-host "Installing AMD HIP SDK"
           $proc = Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -PassThru
-          $completed = $proc.WaitForExit(600000)
-          if (-not $completed) {
-              Write-Error "ROCm installation timed out after 10 minutes. Killing the process"
-              $proc.Kill()
-              exit 1
-          }
-          if ($proc.ExitCode -ne 0) {
-              Write-Error "ROCm installation failed with exit code $($proc.ExitCode)"
-              exit 1
-          }
+          $proc.WaitForExit(600000)
           write-host "Completed AMD HIP SDK installation"
 
       - name: Verify ROCm
         id: verify
         run: |
-          # Find and test ROCm installation
-          $clangPath = Get-ChildItem 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | Select-Object -First 1
-          if (-not $clangPath) {
-            Write-Error "ROCm installation not found"
-            exit 1
-          }
-          & $clangPath.FullName --version
+          & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version
 
       - name: Build
         id: cmake_build
@@ -612,12 +585,9 @@ jobs:
             -DLLAMA_CURL=OFF
           cmake --build build --target ggml-hip -j ${env:NUMBER_OF_PROCESSORS}
           md "build\bin\rocblas\library\"
-          md "build\bin\hipblaslt\library"
           cp "${env:HIP_PATH}\bin\hipblas.dll" "build\bin\"
-          cp "${env:HIP_PATH}\bin\hipblaslt.dll" "build\bin\"
           cp "${env:HIP_PATH}\bin\rocblas.dll" "build\bin\"
           cp "${env:HIP_PATH}\bin\rocblas\library\*" "build\bin\rocblas\library\"
-          cp "${env:HIP_PATH}\bin\hipblaslt\library\*" "build\bin\hipblaslt\library\"
 
       - name: Pack artifacts
         id: pack_artifacts

.github/workflows/server.yml

@@ -76,206 +76,51 @@ jobs:
         run: |
           pip install -r tools/server/tests/requirements.txt
 
-  webui-setup:
-    name: WebUI Setup
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
+      # Setup nodejs (to be used for verifying bundled index.html)
+      - uses: actions/setup-node@v4
         with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+          node-version: '22.11.0'
 
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
-
-      - name: Cache node_modules
-        uses: actions/cache@v4
-        id: cache-node-modules
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Install dependencies
-        if: steps.cache-node-modules.outputs.cache-hit != 'true'
-        run: npm ci
-        working-directory: tools/server/webui
-
-  webui-check:
-    needs: webui-setup
-    name: WebUI Check
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-
-      - name: Restore node_modules cache
-        uses: actions/cache@v4
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Run type checking
-        run: npm run check
-        working-directory: tools/server/webui
-
-      - name: Run linting
-        run: npm run lint
-        working-directory: tools/server/webui
-
-  webui-build:
-    needs: webui-check
-    name: WebUI Build
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-
-      - name: Restore node_modules cache
-        uses: actions/cache@v4
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Build application
-        run: npm run build
-        working-directory: tools/server/webui
-
-  webui-tests:
-    needs: webui-build
-    name: Run WebUI tests
-    permissions:
-      contents: read
-
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-
-      - name: Restore node_modules cache
-        uses: actions/cache@v4
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Install Playwright browsers
-        run: npx playwright install --with-deps
-        working-directory: tools/server/webui
-
-      - name: Build Storybook
-        run: npm run build-storybook
-        working-directory: tools/server/webui
-
-      - name: Run Client tests
-        run: npm run test:client
-        working-directory: tools/server/webui
-
-      - name: Run Server tests
-        run: npm run test:server
-        working-directory: tools/server/webui
-
-      - name: Run UI tests
-        run: npm run test:ui
-        working-directory: tools/server/webui
-
-      - name: Run E2E tests
-        run: npm run test:e2e
-        working-directory: tools/server/webui
-
-  server-build:
-    needs: [webui-tests]
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
-        build_type: [RelWithDebInfo]
-        include:
-          - build_type: Release
-            sanitizer: ""
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
-
-    steps:
-      - name: Dependencies
-        id: depends
+      - name: WebUI - Install dependencies
+        id: webui_lint
         run: |
-          sudo apt-get update
-          sudo apt-get -y install \
-            build-essential \
-            xxd \
-            git \
-            cmake \
-            curl \
-            wget \
-            language-pack-en \
-            libcurl4-openssl-dev
+          cd tools/server/webui
+          npm ci
 
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
+      - name: WebUI - Check code format
+        id: webui_format
         run: |
-          pip install -r tools/server/tests/requirements.txt
+          git config --global --add safe.directory $(realpath .)
+          cd tools/server/webui
+          git status
 
-      - name: Setup Node.js for WebUI
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
+          npm run format
+          git status
+          modified_files="$(git status -s)"
+          echo "Modified files: ${modified_files}"
+          if [ -n "${modified_files}" ]; then
+            echo "Files do not follow coding style. To fix: npm run format"
+            echo "${modified_files}"
+            exit 1
+          fi
 
-      - name: Install WebUI dependencies
-        run: npm ci
-        working-directory: tools/server/webui
+      - name: Verify bundled index.html
+        id: verify_server_index_html
+        run: |
+          git config --global --add safe.directory $(realpath .)
+          cd tools/server/webui
+          git status
 
-      - name: Build WebUI
-        run: npm run build
-        working-directory: tools/server/webui
+          npm run build
+          git status
+          modified_files="$(git status -s)"
+          echo "Modified files: ${modified_files}"
+          if [ -n "${modified_files}" ]; then
+            echo "Repository is dirty or server/webui is not built as expected"
+            echo "Hint: You may need to follow Web UI build guide in server/README.md"
+            echo "${modified_files}"
+            exit 1
+          fi
 
       - name: Build (no OpenMP)
         id: cmake_build_no_openmp

.gitignore

@@ -148,7 +148,3 @@ poetry.toml
 /run-vim.sh
 /run-chat.sh
 .ccache/
-
-# Code Workspace
-*.code-workspace
-

.windsurf/rules/css-architecture.md

@@ -1,7 +0,0 @@
----
-trigger: manual
----
-
-#### Tailwind & CSS
-
--   We are using Tailwind v4 which uses oklch colors so we now want to refer to the CSS vars directly, without wrapping it with any color function like `hsla/hsl`, `rgba` etc.

.windsurf/rules/sveltekit-architecture.md

@@ -1,48 +0,0 @@
----
-trigger: manual
----
-
-# Coding rules
-
-## Svelte & SvelteKit
-
-### Services vs Stores Separation Pattern
-
-#### `lib/services/` - Pure Business Logic
-
--   **Purpose**: Stateless business logic and external communication
--   **Contains**:
-    -   API calls to external services (ApiService)
-    -   Pure business logic functions (ChatService, etc.)
--   **Rules**:
-    -   NO Svelte runes ($state, $derived, $effect)
-    -   NO reactive state management
-    -   Pure functions and classes only
-    -   Can import types but not stores
-    -   Focus on "how" - implementation details
-
-#### `lib/stores/` - Reactive State Management
-
--   **Purpose**: Svelte-specific reactive state with runes
--   **Contains**:
-    -   Reactive state classes with $state, $derived, $effect
-    -   Database operations (DatabaseStore)
-    -   UI-focused state management
-    -   Store orchestration logic
--   **Rules**:
-    -   USE Svelte runes for reactivity
-    -   Import and use services for business logic
-    -   NO direct database operations
-    -   NO direct API calls (use services)
-    -   Focus on "what" - reactive state for UI
-
-#### Enforcement
-
--   Services should be testable without Svelte
--   Stores should leverage Svelte's reactivity system
--   Clear separation: services handle data, stores handle state
--   Services can be reused across multiple stores
-
-#### Misc
-
--   Always use `let` for $derived state variables

.windsurf/rules/tests.md

@@ -1,9 +0,0 @@
----
-trigger: manual
----
-
-# Automated Tests
-
-## General rules
-
--   NEVER include any test code in the production code - we should always have it in a separate dedicated files

.windsurf/rules/typescript-architecture.md

@@ -1,7 +0,0 @@
----
-trigger: manual
----
-
-## TypeScript
-
--   Add JSDocs for functions

CMakeLists.txt

@@ -58,12 +58,6 @@ if (MSVC)
     add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/bigobj>")
 endif()
 
-if (CMAKE_SYSTEM_NAME STREQUAL "iOS")
-    set(LLAMA_TOOLS_INSTALL_DEFAULT OFF)
-else()
-    set(LLAMA_TOOLS_INSTALL_DEFAULT ${LLAMA_STANDALONE})
-endif()
-
 #
 # option list
 #
@@ -88,7 +82,6 @@ option(LLAMA_BUILD_TESTS    "llama: build tests"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_TOOLS    "llama: build tools"          ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES "llama: build examples"       ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_SERVER   "llama: build server example" ${LLAMA_STANDALONE})
-option(LLAMA_TOOLS_INSTALL  "llama: install tools"        ${LLAMA_TOOLS_INSTALL_DEFAULT})
 
 # 3rd party libs
 option(LLAMA_CURL       "llama: use libcurl to download model from an URL" ON)

CONTRIBUTING.md

@@ -16,9 +16,6 @@
 - Use the following format for the squashed commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
 - Optionally pick a `<module>` from here: https://github.com/ggml-org/llama.cpp/wiki/Modules
 - Consider adding yourself to [CODEOWNERS](CODEOWNERS)
-- Let authors, who are also collaborators, merge their own PRs
-- When merging a PR by a contributor, make sure you have a good understanding of the changes
-- Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you)
 
 # Coding guidelines
 

README.md

@@ -137,7 +137,6 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 - [X] [Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview)
 - [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
 - [x] [LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38)
-- [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
 
 #### Multimodal
 

ci/run.sh

@@ -45,7 +45,7 @@ SRC=`pwd`
 CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON -DLLAMA_CURL=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON -DGGML_METAL_USE_BF16=ON"
 fi
 
 if [ ! -z ${GG_BUILD_CUDA} ]; then
@@ -270,9 +270,7 @@ function gg_run_ctest_with_model_debug {
     local model; model=$(gg_get_model)
     cd build-ci-debug
     set -e
-
     (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
-
     set +e
     cd ..
 }
@@ -283,15 +281,7 @@ function gg_run_ctest_with_model_release {
     local model; model=$(gg_get_model)
     cd build-ci-release
     set -e
-
     (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
-
-    # test memory leaks
-    #if [[ ! -z ${GG_BUILD_METAL} ]]; then
-    #    # TODO: this hangs for some reason ...
-    #    (time leaks -quiet -atExit -- ./bin/test-thread-safety -m $model --parallel 2 -t 2 -p "hello") 2>&1 | tee -a $OUT/${ci}-leaks.log
-    #fi
-
     set +e
     cd ..
 }
@@ -396,10 +386,10 @@ function gg_run_open_llama_7b_v2 {
 
     (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -530,8 +520,8 @@ function gg_run_pythia_1_4b {
 
     (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -661,10 +651,10 @@ function gg_run_pythia_2_8b {
 
     (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on  ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0     ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -870,7 +860,10 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
 fi
 
 ret=0
-test $ret -eq 0 && gg_run ctest_debug
+if [ -z ${GG_BUILD_SYCL} ]; then
+    # SYCL build breaks with debug build flags
+    test $ret -eq 0 && gg_run ctest_debug
+fi
 test $ret -eq 0 && gg_run ctest_release
 
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
@@ -878,7 +871,9 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
     test $ret -eq 0 && gg_run rerank_tiny
 
     if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
-        test $ret -eq 0 && gg_run test_scripts_debug
+        if [ -z ${GG_BUILD_SYCL} ]; then
+            test $ret -eq 0 && gg_run test_scripts_debug
+        fi
         test $ret -eq 0 && gg_run test_scripts_release
     fi
 
@@ -889,7 +884,9 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
             test $ret -eq 0 && gg_run pythia_2_8b
             #test $ret -eq 0 && gg_run open_llama_7b_v2
         fi
-        test $ret -eq 0 && gg_run ctest_with_model_debug
+        if [ -z ${GG_BUILD_SYCL} ]; then
+            test $ret -eq 0 && gg_run ctest_with_model_debug
+        fi
         test $ret -eq 0 && gg_run ctest_with_model_release
     fi
 fi

common/arg.cpp

@@ -57,32 +57,12 @@ static std::string read_file(const std::string & fname) {
 }
 
 static void write_file(const std::string & fname, const std::string & content) {
-    const std::string fname_tmp = fname + ".tmp";
-    std::ofstream     file(fname_tmp);
+    std::ofstream file(fname);
     if (!file) {
         throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
     }
-
-    try {
-        file << content;
-        file.close();
-
-        // Makes write atomic
-        if (rename(fname_tmp.c_str(), fname.c_str()) != 0) {
-            LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, fname_tmp.c_str(), fname.c_str());
-            // If rename fails, try to delete the temporary file
-            if (remove(fname_tmp.c_str()) != 0) {
-                LOG_ERR("%s: unable to delete temporary file: %s\n", __func__, fname_tmp.c_str());
-            }
-        }
-    } catch (...) {
-        // If anything fails, try to delete the temporary file
-        if (remove(fname_tmp.c_str()) != 0) {
-            LOG_ERR("%s: unable to delete temporary file: %s\n", __func__, fname_tmp.c_str());
-        }
-
-        throw std::runtime_error(string_format("error: failed to write file '%s'\n", fname.c_str()));
-    }
+    file << content;
+    file.close();
 }
 
 common_arg & common_arg::set_examples(std::initializer_list<enum llama_example> examples) {
@@ -237,294 +217,250 @@ struct curl_slist_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__);
+#define CURL_MAX_RETRY 3
+#define CURL_RETRY_DELAY_SECONDS 2
+
+static bool curl_perform_with_retry(const std::string & url, CURL * curl, int max_attempts, int retry_delay_seconds, const char * method_name) {
+    int remaining_attempts = max_attempts;
+
+    while (remaining_attempts > 0) {
+        LOG_INF("%s: %s %s (attempt %d of %d)...\n", __func__ , method_name, url.c_str(), max_attempts - remaining_attempts + 1, max_attempts);
+
+        CURLcode res = curl_easy_perform(curl);
+        if (res == CURLE_OK) {
+            return true;
+        }
+
+        int exponential_backoff_delay = std::pow(retry_delay_seconds, max_attempts - remaining_attempts) * 1000;
+        LOG_WRN("%s: curl_easy_perform() failed: %s, retrying after %d milliseconds...\n", __func__, curl_easy_strerror(res), exponential_backoff_delay);
+
+        remaining_attempts--;
+        if (remaining_attempts == 0) break;
+        std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
     }
 
-    return res;
+    LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
+
+    return false;
 }
 
-// Send a HEAD request to retrieve the etag and last-modified headers
-struct common_load_model_from_url_headers {
+// download one single file from remote URL to local path
+static bool common_download_file_single(const std::string & url, const std::string & path, const std::string & bearer_token, bool offline) {
+    // Check if the file already exists locally
+    auto file_exists = std::filesystem::exists(path);
+
+    // If the file exists, check its JSON metadata companion file.
+    std::string metadata_path = path + ".json";
+    nlohmann::json metadata; // TODO @ngxson : get rid of this json, use regex instead
     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;
+    if (file_exists) {
+        if (offline) {
+            LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
+            return true; // skip verification/downloading
         }
+        // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
+        std::ifstream metadata_in(metadata_path);
+        if (metadata_in.good()) {
+            try {
+                metadata_in >> metadata;
+                LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str());
+                if (metadata.contains("etag") && metadata.at("etag").is_string()) {
+                    etag = metadata.at("etag");
+                }
+                if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
+                    last_modified = metadata.at("lastModified");
+                }
+            } catch (const nlohmann::json::exception & e) {
+                LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
+            }
+        }
+        // if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again)
+    } else {
+        if (offline) {
+            LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
+            return false;
+        }
+        LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
     }
 
-    return n_items;
-}
+    // 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;
+    };
 
-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));
-}
+    common_load_model_from_url_headers headers;
+    bool head_request_ok = false;
+    bool should_download = !file_exists; // by default, we should download if the file does not exist
 
-// 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) {
+    // Initialize libcurl
+    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
+    curl_slist_ptr http_headers;
     if (!curl) {
         LOG_ERR("%s: error initializing libcurl\n", __func__);
         return false;
     }
 
+    // Set the URL, allow to follow http redirection
+    curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
+    curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
+
     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());
+        http_headers.ptr = curl_slist_append(http_headers.ptr, auth_header.c_str());
+    }
+    curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
+
+#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.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
+#endif
+
+    typedef size_t(*CURLOPT_HEADERFUNCTION_PTR)(char *, size_t, size_t, void *);
+    auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t {
+        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);
+
+        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;
+            }
+        }
+        return n_items;
+    };
+
+    curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb
+    curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress
+    curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast<CURLOPT_HEADERFUNCTION_PTR>(header_callback));
+    curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
+
+    // we only allow retrying once for HEAD requests
+    // this is for the use case of using running offline (no internet), retrying can be annoying
+    bool was_perform_successful = curl_perform_with_retry(url, curl.get(), 1, 0, "HEAD");
+    if (!was_perform_successful) {
+        head_request_ok = false;
     }
 
-    curl_easy_setopt(curl, CURLOPT_HTTPHEADER, http_headers.ptr);
-    common_curl_easy_setopt_head(curl, url);
-    return common_curl_perf(curl) == CURLE_OK;
-}
+    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;
+    }
 
-// download one single file from remote URL to local path
-static bool common_download_file_single(const std::string & url,
-                                        const std::string & path,
-                                        const std::string & bearer_token,
-                                        bool                offline) {
-    // If the file exists, check its JSON metadata companion file.
-    std::string metadata_path = path + ".json";
-    static const int max_attempts        = 3;
-    static const int retry_delay_seconds = 2;
-    for (int i = 0; i < max_attempts; ++i) {
-        nlohmann::json metadata;  // TODO @ngxson : get rid of this json, use regex instead
-        std::string    etag;
-        std::string    last_modified;
+    // 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
+    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;
+        } else if (!last_modified.empty() && last_modified != headers.last_modified) {
+            LOG_WRN("%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str());
+            should_download = true;
+        }
+    }
 
-        // Check if the file already exists locally
-        const auto file_exists = std::filesystem::exists(path);
+    if (should_download) {
+        std::string path_temporary = path + ".downloadInProgress";
         if (file_exists) {
-            if (offline) {
-                LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
-                return true;  // skip verification/downloading
-            }
-            // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
-            std::ifstream metadata_in(metadata_path);
-            if (metadata_in.good()) {
-                try {
-                    metadata_in >> metadata;
-                    LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(),
-                            metadata.dump().c_str());
-                    if (metadata.contains("etag") && metadata.at("etag").is_string()) {
-                        etag = metadata.at("etag");
-                    }
-                    if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) {
-                        last_modified = metadata.at("lastModified");
-                    }
-                } catch (const nlohmann::json::exception & e) {
-                    LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
-                }
-            }
-            // if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again)
-        } else {
-            if (offline) {
-                LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
+            LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
+            if (remove(path.c_str()) != 0) {
+                LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
                 return false;
             }
-            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
+        // Set the output file
 
-        // Initialize libcurl
-        curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
-        common_load_model_from_url_headers headers;
-        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
-        curl_slist_ptr http_headers;
-        const bool     was_perform_successful = common_download_head(curl.get(), http_headers, url, bearer_token);
+        struct FILE_deleter {
+            void operator()(FILE * f) const {
+                fclose(f);
+            }
+        };
+
+        std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "wb"));
+        if (!outfile) {
+            LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path.c_str());
+            return false;
+        }
+
+        typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * data, size_t size, size_t nmemb, void * fd);
+        auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t {
+            return fwrite(data, size, nmemb, (FILE *)fd);
+        };
+        curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L);
+        curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
+        curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get());
+
+        //  display download progress
+        curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L);
+
+        // helper function to hide password in URL
+        auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string {
+            std::size_t protocol_pos = url.find("://");
+            if (protocol_pos == std::string::npos) {
+                return url;  // Malformed URL
+            }
+
+            std::size_t at_pos = url.find('@', protocol_pos + 3);
+            if (at_pos == std::string::npos) {
+                return url;  // No password in URL
+            }
+
+            return url.substr(0, protocol_pos + 3) + "********" + url.substr(at_pos);
+        };
+
+        // 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.c_str(), headers.etag.c_str(), headers.last_modified.c_str());
+        bool was_perform_successful = curl_perform_with_retry(url, curl.get(), CURL_MAX_RETRY, CURL_RETRY_DELAY_SECONDS, "GET");
         if (!was_perform_successful) {
-            head_request_ok = false;
+            return 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;
+        curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
+        if (http_code < 200 || http_code >= 400) {
+            LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code);
+            return false;
         }
 
-        // if 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;
-            } else if (!last_modified.empty() && last_modified != headers.last_modified) {
-                LOG_WRN("%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__,
-                        last_modified.c_str(), headers.last_modified.c_str());
-                should_download              = true;
-                should_download_from_scratch = true;
-            }
+        // Causes file to be closed explicitly here before we rename it.
+        outfile.reset();
+
+        // Write the updated JSON metadata file.
+        metadata.update({
+            {"url", url},
+            {"etag", headers.etag},
+            {"lastModified", headers.last_modified}
+        });
+        write_file(metadata_path, metadata.dump(4));
+        LOG_DBG("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
+
+        if (rename(path_temporary.c_str(), path.c_str()) != 0) {
+            LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
+            return false;
         }
-
-        const bool accept_ranges_supported = !headers.accept_ranges.empty() && headers.accept_ranges != "none";
-        if (should_download) {
-            if (file_exists &&
-                !accept_ranges_supported) {  // Resumable downloads not supported, delete and start again.
-                LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
-                if (remove(path.c_str()) != 0) {
-                    LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
-                    return false;
-                }
-            }
-
-            const std::string path_temporary = path + ".downloadInProgress";
-            if (should_download_from_scratch) {
-                if (std::filesystem::exists(path_temporary)) {
-                    if (remove(path_temporary.c_str()) != 0) {
-                        LOG_ERR("%s: unable to delete file: %s\n", __func__, path_temporary.c_str());
-                        return false;
-                    }
-                }
-
-                if (std::filesystem::exists(path)) {
-                    if (remove(path.c_str()) != 0) {
-                        LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
-                        return false;
-                    }
-                }
-            }
-
-            // Write the updated JSON metadata file.
-            metadata.update({
-                { "url",          url                   },
-                { "etag",         headers.etag          },
-                { "lastModified", headers.last_modified }
-            });
-            write_file(metadata_path, metadata.dump(4));
-            LOG_DBG("%s: file metadata saved: %s\n", __func__, metadata_path.c_str());
-
-            // start the download
-            LOG_INF("%s: trying to download model from %s to %s (server_etag:%s, server_last_modified:%s)...\n",
-                    __func__, llama_download_hide_password_in_url(url).c_str(), path_temporary.c_str(),
-                    headers.etag.c_str(), headers.last_modified.c_str());
-            const bool was_pull_successful = common_pull_file(curl.get(), path_temporary);
-            if (!was_pull_successful) {
-                if (i + 1 < max_attempts) {
-                    const int exponential_backoff_delay = std::pow(retry_delay_seconds, i) * 1000;
-                    LOG_WRN("%s: retrying after %d milliseconds...\n", __func__, exponential_backoff_delay);
-                    std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
-                } else {
-                    LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
-                }
-
-                continue;
-            }
-
-            long http_code = 0;
-            curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code);
-            if (http_code < 200 || http_code >= 400) {
-                LOG_ERR("%s: invalid http status code received: %ld\n", __func__, http_code);
-                return false;
-            }
-
-            if (rename(path_temporary.c_str(), path.c_str()) != 0) {
-                LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
-                return false;
-            }
-        } else {
-            LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
-        }
-
-        break;
+    } else {
+        LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
     }
 
     return true;
@@ -809,124 +745,6 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string &
 
 #endif // LLAMA_USE_CURL
 
-//
-// Docker registry functions
-//
-
-static std::string common_docker_get_token(const std::string & repo) {
-    std::string url = "https://auth.docker.io/token?service=registry.docker.io&scope=repository:" + repo + ":pull";
-
-    common_remote_params params;
-    auto                 res = common_remote_get_content(url, params);
-
-    if (res.first != 200) {
-        throw std::runtime_error("Failed to get Docker registry token, HTTP code: " + std::to_string(res.first));
-    }
-
-    std::string            response_str(res.second.begin(), res.second.end());
-    nlohmann::ordered_json response = nlohmann::ordered_json::parse(response_str);
-
-    if (!response.contains("token")) {
-        throw std::runtime_error("Docker registry token response missing 'token' field");
-    }
-
-    return response["token"].get<std::string>();
-}
-
-static std::string common_docker_resolve_model(const std::string & docker) {
-    // Parse ai/smollm2:135M-Q4_0
-    size_t      colon_pos = docker.find(':');
-    std::string repo, tag;
-    if (colon_pos != std::string::npos) {
-        repo = docker.substr(0, colon_pos);
-        tag  = docker.substr(colon_pos + 1);
-    } else {
-        repo = docker;
-        tag  = "latest";
-    }
-
-    // ai/ is the default
-    size_t      slash_pos = docker.find('/');
-    if (slash_pos == std::string::npos) {
-        repo.insert(0, "ai/");
-    }
-
-    LOG_INF("%s: Downloading Docker Model: %s:%s\n", __func__, repo.c_str(), tag.c_str());
-    try {
-        // --- helper: digest validation ---
-        auto validate_oci_digest = [](const std::string & digest) -> std::string {
-            // Expected: algo:hex ; start with sha256 (64 hex chars)
-            // You can extend this map if supporting other algorithms in future.
-            static const std::regex re("^sha256:([a-fA-F0-9]{64})$");
-            std::smatch m;
-            if (!std::regex_match(digest, m, re)) {
-                throw std::runtime_error("Invalid OCI digest format received in manifest: " + digest);
-            }
-            // normalize hex to lowercase
-            std::string normalized = digest;
-            std::transform(normalized.begin()+7, normalized.end(), normalized.begin()+7, [](unsigned char c){
-                return std::tolower(c);
-            });
-            return normalized;
-        };
-
-        std::string token = common_docker_get_token(repo);  // Get authentication token
-
-        // Get manifest
-        const std::string    url_prefix = "https://registry-1.docker.io/v2/" + repo;
-        std::string          manifest_url = url_prefix + "/manifests/" + tag;
-        common_remote_params manifest_params;
-        manifest_params.headers.push_back("Authorization: Bearer " + token);
-        manifest_params.headers.push_back(
-            "Accept: application/vnd.docker.distribution.manifest.v2+json,application/vnd.oci.image.manifest.v1+json");
-        auto manifest_res = common_remote_get_content(manifest_url, manifest_params);
-        if (manifest_res.first != 200) {
-            throw std::runtime_error("Failed to get Docker manifest, HTTP code: " + std::to_string(manifest_res.first));
-        }
-
-        std::string            manifest_str(manifest_res.second.begin(), manifest_res.second.end());
-        nlohmann::ordered_json manifest = nlohmann::ordered_json::parse(manifest_str);
-        std::string            gguf_digest;  // Find the GGUF layer
-        if (manifest.contains("layers")) {
-            for (const auto & layer : manifest["layers"]) {
-                if (layer.contains("mediaType")) {
-                    std::string media_type = layer["mediaType"].get<std::string>();
-                    if (media_type == "application/vnd.docker.ai.gguf.v3" ||
-                        media_type.find("gguf") != std::string::npos) {
-                        gguf_digest = layer["digest"].get<std::string>();
-                        break;
-                    }
-                }
-            }
-        }
-
-        if (gguf_digest.empty()) {
-            throw std::runtime_error("No GGUF layer found in Docker manifest");
-        }
-
-        // Validate & normalize digest
-        gguf_digest = validate_oci_digest(gguf_digest);
-        LOG_DBG("%s: Using validated digest: %s\n", __func__, gguf_digest.c_str());
-
-        // Prepare local filename
-        std::string model_filename = repo;
-        std::replace(model_filename.begin(), model_filename.end(), '/', '_');
-        model_filename += "_" + tag + ".gguf";
-        std::string local_path = fs_get_cache_file(model_filename);
-
-        const std::string blob_url = url_prefix + "/blobs/" + gguf_digest;
-        if (!common_download_file_single(blob_url, local_path, token, false)) {
-            throw std::runtime_error("Failed to download Docker Model");
-        }
-
-        LOG_INF("%s: Downloaded Docker Model to: %s\n", __func__, local_path.c_str());
-        return local_path;
-    } catch (const std::exception & e) {
-        LOG_ERR("%s: Docker Model download failed: %s\n", __func__, e.what());
-        throw;
-    }
-}
-
 //
 // utils
 //
@@ -977,9 +795,7 @@ static handle_model_result common_params_handle_model(
     handle_model_result result;
     // handle pre-fill default model path and url based on hf_repo and hf_file
     {
-        if (!model.docker_repo.empty()) {  // Handle Docker URLs by resolving them to local paths
-            model.path = common_docker_resolve_model(model.docker_repo);
-        } else if (!model.hf_repo.empty()) {
+        if (!model.hf_repo.empty()) {
             // short-hand to avoid specifying --hf-file -> default it to --model
             if (model.hf_file.empty()) {
                 if (model.path.empty()) {
@@ -1368,7 +1184,7 @@ static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & val
     } else {
         for (const auto & device : dev_names) {
             auto * dev = ggml_backend_dev_by_name(device.c_str());
-            if (!dev || ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
+            if (!dev || ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_GPU) {
                 throw std::invalid_argument(string_format("invalid device: %s", device.c_str()));
             }
             devices.push_back(dev);
@@ -1378,7 +1194,7 @@ static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & val
     return devices;
 }
 
-static void add_rpc_devices(const std::string & servers) {
+static void add_rpc_devices(std::string servers) {
     auto rpc_servers = string_split<std::string>(servers, ',');
     if (rpc_servers.empty()) {
         throw std::invalid_argument("no RPC servers specified");
@@ -1447,18 +1263,6 @@ static std::string list_builtin_chat_templates() {
     return msg.str();
 }
 
-static bool is_truthy(const std::string & value) {
-    return value == "on" || value == "enabled" || value == "1";
-}
-
-static bool is_falsey(const std::string & value) {
-    return value == "off" || value == "disabled" || value == "0";
-}
-
-static bool is_autoy(const std::string & value) {
-    return value == "auto" || value == "-1";
-}
-
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
     // load dynamic backends
     ggml_backend_load_all();
@@ -1740,21 +1544,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.n_chunks = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY, LLAMA_EXAMPLE_RETRIEVAL}));
-    add_opt(common_arg({ "-fa", "--flash-attn" }, "[on|off|auto]",
-                       string_format("set Flash Attention use ('on', 'off', or 'auto', default: '%s')",
-                                     llama_flash_attn_type_name(params.flash_attn_type)),
-                       [](common_params & params, const std::string & value) {
-                           if (is_truthy(value)) {
-                               params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED;
-                           } else if (is_falsey(value)) {
-                               params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_DISABLED;
-                           } else if (is_autoy(value)) {
-                               params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_AUTO;
-                           } else {
-                               throw std::runtime_error(
-                                   string_format("error: unkown value for --flash-attn: '%s'\n", value.c_str()));
-                           }
-                       }).set_env("LLAMA_ARG_FLASH_ATTN"));
+    add_opt(common_arg(
+        {"-fa", "--flash-attn"},
+        string_format("enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled"),
+        [](common_params & params) {
+            params.flash_attn = true;
+        }
+    ).set_env("LLAMA_ARG_FLASH_ATTN"));
     add_opt(common_arg(
         {"-p", "--prompt"}, "PROMPT",
         "prompt to start generation with; for system message, use -sys",
@@ -1768,7 +1564,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.system_prompt = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_DIFFUSION}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
         {"--no-perf"},
         string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
@@ -2580,15 +2376,24 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--list-devices"},
         "print list of available devices and exit",
         [](common_params &) {
-            std::vector<ggml_backend_dev_t> devices;
+            std::vector<ggml_backend_dev_t> rpc_devices;
+            std::vector<ggml_backend_dev_t> all_devices;
             for (size_t i = 0; i < ggml_backend_dev_count(); ++i) {
                 auto * dev = ggml_backend_dev_get(i);
-                if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) {
-                    devices.push_back(dev);
+                if (ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_GPU) {
+                    ggml_backend_reg_t reg = ggml_backend_dev_backend_reg(dev);
+                    if (ggml_backend_reg_name(reg) == std::string("RPC")) {
+                        rpc_devices.push_back(dev);
+                    } else {
+                        all_devices.push_back(dev);
+                    }
                 }
             }
+            // insert RPC devices in front
+            all_devices.insert(all_devices.begin(), rpc_devices.begin(), rpc_devices.end());
             printf("Available devices:\n");
-            for (auto * dev : devices) {
+            for (size_t i = 0; i < all_devices.size(); ++i) {
+                auto * dev = all_devices[i];
                 size_t free, total;
                 ggml_backend_dev_memory(dev, &free, &total);
                 printf("  %s: %s (%zu MiB, %zu MiB free)\n", ggml_backend_dev_name(dev), ggml_backend_dev_description(dev), total / 1024 / 1024, free / 1024 / 1024);
@@ -2612,7 +2417,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--cpu-moe", "-cmoe"},
         "keep all Mixture of Experts (MoE) weights in the CPU",
         [](common_params & params) {
-            params.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
+            params.tensor_buft_overrides.push_back({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
         }
     ).set_env("LLAMA_ARG_CPU_MOE"));
     add_opt(common_arg(
@@ -2625,7 +2430,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             for (int i = 0; i < value; ++i) {
                 // keep strings alive and avoid leaking memory by storing them in a static vector
                 static std::list<std::string> buft_overrides;
-                buft_overrides.push_back(llm_ffn_exps_block_regex(i));
+                buft_overrides.push_back(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
                 params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()});
             }
         }
@@ -2634,7 +2439,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--cpu-moe-draft", "-cmoed"},
         "keep all Mixture of Experts (MoE) weights in the CPU for the draft model",
         [](common_params & params) {
-            params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
+            params.speculative.tensor_buft_overrides.push_back({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
     add_opt(common_arg(
@@ -2646,14 +2451,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
             for (int i = 0; i < value; ++i) {
                 static std::list<std::string> buft_overrides_draft;
-                buft_overrides_draft.push_back(llm_ffn_exps_block_regex(i));
+                buft_overrides_draft.push_back(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
                 params.speculative.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
             }
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
-        string_format("max. number of layers to store in VRAM (default: %d)", params.n_gpu_layers),
+        "number of layers to store in VRAM",
         [](common_params & params, int value) {
             params.n_gpu_layers = value;
             if (!llama_supports_gpu_offload()) {
@@ -2750,7 +2555,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora"}, "FNAME",
         "path to LoRA adapter (can be repeated to use multiple adapters)",
         [](common_params & params, const std::string & value) {
-            params.lora_adapters.push_back({ std::string(value), 1.0, "", "", nullptr });
+            params.lora_adapters.push_back({ std::string(value), 1.0, nullptr });
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
@@ -2758,7 +2563,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--lora-scaled"}, "FNAME", "SCALE",
         "path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
         [](common_params & params, const std::string & fname, const std::string & scale) {
-            params.lora_adapters.push_back({ fname, std::stof(scale), "", "", nullptr });
+            params.lora_adapters.push_back({ fname, std::stof(scale), nullptr });
         }
         // we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
@@ -2811,15 +2616,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.url = value;
         }
     ).set_env("LLAMA_ARG_MODEL_URL"));
-    add_opt(common_arg(
-        { "-dr", "--docker-repo" }, "[<repo>/]<model>[:quant]",
-        "Docker Hub model repository. repo is optional, default to ai/. quant is optional, default to :latest.\n"
-        "example: gemma3\n"
-        "(default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.model.docker_repo = value;
-        }
-    ).set_env("LLAMA_ARG_DOCKER_REPO"));
     add_opt(common_arg(
         {"-hf", "-hfr", "--hf-repo"}, "<user>/<model>[:quant]",
         "Hugging Face model repository; quant is optional, case-insensitive, default to Q4_K_M, or falls back to the first file in the repo if Q4_K_M doesn't exist.\n"
@@ -3158,13 +2954,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.endpoint_metrics = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_METRICS"));
-    add_opt(common_arg(
-        {"--props"},
-        string_format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.endpoint_props = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS"));
     add_opt(common_arg(
         {"--slots"},
         string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
@@ -3172,6 +2961,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.endpoint_slots = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS"));
+    add_opt(common_arg(
+        {"--props"},
+        string_format("enable changing global properties via POST /props (default: %s)", params.endpoint_props ? "enabled" : "disabled"),
+        [](common_params & params) {
+            params.endpoint_props = true;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS"));
     add_opt(common_arg(
         {"--no-slots"},
         "disables slots monitoring endpoint",
@@ -3330,21 +3126,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             common_log_set_file(common_log_main(), value.c_str());
         }
     ));
-    add_opt(common_arg({ "--log-colors" }, "[on|off|auto]",
-                       "Set colored logging ('on', 'off', or 'auto', default: 'auto')\n"
-                       "'auto' enables colors when output is to a terminal",
-                       [](common_params &, const std::string & value) {
-                           if (is_truthy(value)) {
-                               common_log_set_colors(common_log_main(), LOG_COLORS_ENABLED);
-                           } else if (is_falsey(value)) {
-                               common_log_set_colors(common_log_main(), LOG_COLORS_DISABLED);
-                           } else if (is_autoy(value)) {
-                               common_log_set_colors(common_log_main(), LOG_COLORS_AUTO);
-                           } else {
-                               throw std::invalid_argument(
-                                   string_format("error: unkown value for --log-colors: '%s'\n", value.c_str()));
-                           }
-                       }).set_env("LLAMA_LOG_COLORS"));
+    add_opt(common_arg(
+        {"--log-colors"},
+        "Enable colored logging",
+        [](common_params &) {
+            common_log_set_colors(common_log_main(), true);
+        }
+    ).set_env("LLAMA_LOG_COLORS"));
     add_opt(common_arg(
         {"-v", "--verbose", "--log-verbose"},
         "Set verbosity level to infinity (i.e. log all messages, useful for debugging)",
@@ -3671,6 +3459,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-1.5B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-1.5b-q8_0.gguf";
             params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3685,6 +3475,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-3B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-3b-q8_0.gguf";
             params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3699,6 +3491,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_repo = "ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF";
             params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf";
             params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3714,7 +3508,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_file = "qwen2.5-coder-7b-q8_0.gguf";
             params.speculative.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
             params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
+            params.speculative.n_gpu_layers = 99;
             params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3730,7 +3527,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_file = "qwen2.5-coder-14b-q8_0.gguf";
             params.speculative.model.hf_repo = "ggml-org/Qwen2.5-Coder-0.5B-Q8_0-GGUF";
             params.speculative.model.hf_file = "qwen2.5-coder-0.5b-q8_0.gguf";
+            params.speculative.n_gpu_layers = 99;
             params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;
@@ -3745,6 +3545,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_repo = "ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF";
             params.model.hf_file = "qwen3-coder-30b-a3b-instruct-q8_0.gguf";
             params.port = 8012;
+            params.n_gpu_layers = 99;
+            params.flash_attn = true;
             params.n_ubatch = 1024;
             params.n_batch = 1024;
             params.n_ctx = 0;

common/chat.cpp

@@ -163,19 +163,6 @@ common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::strin
     throw std::runtime_error("Invalid tool_choice: " + tool_choice);
 }
 
-bool common_chat_templates_support_enable_thinking(const common_chat_templates * chat_templates) {
-    common_chat_templates_inputs dummy_inputs;
-    common_chat_msg msg;
-    msg.role = "user";
-    msg.content = "test";
-    dummy_inputs.messages = {msg};
-    dummy_inputs.enable_thinking = false;
-    const auto rendered_no_thinking = common_chat_templates_apply(chat_templates, dummy_inputs);
-    dummy_inputs.enable_thinking = true;
-    const auto rendered_with_thinking = common_chat_templates_apply(chat_templates, dummy_inputs);
-    return rendered_no_thinking.prompt != rendered_with_thinking.prompt;
-}
-
 template <>
 std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const json & messages) {
     std::vector<common_chat_msg> msgs;
@@ -631,13 +618,10 @@ const char * common_chat_format_name(common_chat_format format) {
         case COMMON_CHAT_FORMAT_FIREFUNCTION_V2: return "FireFunction v2";
         case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2: return "Functionary v3.2";
         case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1: return "Functionary v3.1 Llama 3.1";
-        case COMMON_CHAT_FORMAT_DEEPSEEK_V3_1: return "DeepSeek V3.1";
         case COMMON_CHAT_FORMAT_HERMES_2_PRO: return "Hermes 2 Pro";
         case COMMON_CHAT_FORMAT_COMMAND_R7B: return "Command R7B";
         case COMMON_CHAT_FORMAT_GRANITE: return "Granite";
         case COMMON_CHAT_FORMAT_GPT_OSS: return "GPT-OSS";
-        case COMMON_CHAT_FORMAT_SEED_OSS: return "Seed-OSS";
-        case COMMON_CHAT_FORMAT_NEMOTRON_V2: return "Nemotron V2";
         default:
             throw std::runtime_error("Unknown chat format");
     }
@@ -699,13 +683,11 @@ static void parse_json_tool_calls(
         size_t from = std::string::npos;
         auto first = true;
         while (true) {
-            auto start_pos = builder.pos();
             auto res = function_regex_start_only && first
                 ? builder.try_consume_regex(*function_regex_start_only)
                 : function_regex
                     ? builder.try_find_regex(*function_regex, from)
                     : std::nullopt;
-
             if (res) {
                 std::string name;
                 if (get_function_name) {
@@ -740,8 +722,6 @@ static void parse_json_tool_calls(
                     return;
                 }
                 throw common_chat_msg_partial_exception("incomplete tool call");
-            } else {
-                builder.move_to(start_pos);
             }
             break;
         }
@@ -1203,67 +1183,6 @@ static common_chat_params common_chat_params_init_llama_3_x(const common_chat_te
     });
     return data;
 }
-
-static common_chat_params common_chat_params_init_nemotron_v2(const common_chat_template & tmpl, const struct templates_params & inputs) {
-    common_chat_params data;
-
-    // Generate the prompt using the apply() function with the template
-    data.prompt = apply(tmpl, inputs);
-    data.format = COMMON_CHAT_FORMAT_NEMOTRON_V2;
-
-    // Handle thinking tags appropriately based on inputs.enable_thinking
-    if (string_ends_with(data.prompt, "<think>\n")) {
-        if (!inputs.enable_thinking) {
-            data.prompt += "</think>";
-        } else {
-            data.thinking_forced_open = true;
-        }
-    }
-
-    // When tools are present, build grammar for the <TOOLCALL> format, similar to CommandR, but without tool call ID
-    if (!inputs.tools.is_null() && inputs.tools.is_array() && !inputs.tools.empty()) {
-        data.grammar_lazy = true;
-        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
-            auto schemas = json::array();
-            foreach_function(inputs.tools, [&](const json & tool) {
-                const auto & function = tool.at("function");
-                schemas.push_back({
-                    { "type",       "object"                                                   },
-                    { "properties",
-                        {
-                            { "name",
-                            {
-                                { "type", "string" },
-                                { "const", function.at("name") },
-                            } },
-                            { "arguments", function.at("parameters") },
-                        }                                                                        },
-                    { "required",   json::array({ "name", "arguments" }) },
-                });
-            });
-            auto schema = json{
-                        { "type",     "array"                                                         },
-                        { "items",    schemas.size() == 1 ? schemas[0] : json{ { "anyOf", schemas } } },
-                        { "minItems", 1                                                               },
-            };
-            if (!inputs.parallel_tool_calls) {
-                schema["maxItems"] = 1;
-            }
-            builder.add_rule("root",
-                                std::string(data.thinking_forced_open ? "( \"</think>\" space )? " : "") +
-                                    "\"<TOOLCALL>\" " + builder.add_schema("tool_calls", schema) +
-                                    " \"</TOOLCALL>\"");
-        });
-        data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
-            // If thinking_forced_open, then we capture the </think> tag in the grammar,
-            // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar)
-            std::string(data.thinking_forced_open ?
-                            "[\\s\\S]*?(</think>\\s*)" :
-                            "(?:<think>[\\s\\S]*?</think>\\s*)?") +
-                "(<TOOLCALL>)[\\s\\S]*" });
-    }
-    return data;
-}
 static void common_chat_parse_llama_3_1(common_chat_msg_parser & builder, bool with_builtin_tools = false) {
     if (!builder.syntax().parse_tool_calls) {
         builder.add_content(builder.consume_rest());
@@ -1393,71 +1312,6 @@ static common_chat_params common_chat_params_init_deepseek_r1(const common_chat_
     }
     return data;
 }
-
-static common_chat_params common_chat_params_init_deepseek_v3_1(const common_chat_template & tmpl, const struct templates_params & inputs) {
-    common_chat_params data;
-
-    // Pass thinking context for DeepSeek V3.1 template
-    json additional_context = {
-        {"thinking", inputs.enable_thinking},
-    };
-
-    auto prompt = apply(tmpl, inputs,
-                       /* messages_override= */ inputs.messages,
-                       /* tools_override= */ std::nullopt,
-                       additional_context);
-    data.prompt = prompt;
-    data.format = COMMON_CHAT_FORMAT_DEEPSEEK_V3_1;
-    if (string_ends_with(data.prompt, "<think>")) {
-        if (!inputs.enable_thinking) {
-            data.prompt += "</think>";
-        } 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);
-                tool_rules.push_back(builder.add_rule(name + "-call",
-                    "( \"<｜tool▁call▁begin｜>\" )? \"" + name + "<｜tool▁sep｜>"
-                    "\" " + builder.add_schema(name + "-args", parameters) + " "
-                    "\"<｜tool▁call▁end｜>\""));
-            });
-            // Distill Qwen 7B & 32B models seem confused re/ syntax of their tool call opening tag,
-            // so we accept common variants (then it's all constrained)
-            builder.add_rule("root",
-                std::string(data.thinking_forced_open ? "( \"</think>\" space )? " : "") +
-                "( \"<｜tool▁calls▁begin｜>\" | \"<｜tool_calls_begin｜>\" | \"<｜tool calls begin｜>\" | \"<｜tool\\\\_calls\\\\_begin｜>\" | \"<｜tool▁calls｜>\" ) "
-                "(" + string_join(tool_rules, " | ") + ")" + (inputs.parallel_tool_calls ? "*" : "") + " "
-                "\"<｜tool▁calls▁end｜>\""
-                " space");
-            data.grammar_triggers.push_back({
-                COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
-                // If thinking_forced_open, then we capture the </think> tag in the grammar,
-                // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar)
-                std::string(data.thinking_forced_open ? "[\\s\\S]*?(</think>\\s*)" : "(?:<think>[\\s\\S]*?</think>\\s*)?") +
-                    "(<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)[\\s\\S]*"
-            });
-            data.preserved_tokens = {
-                "<think>",
-                "</think>",
-                "<｜tool▁calls▁begin｜>",
-                "<｜tool▁call▁begin｜>",
-                "<｜tool▁sep｜>",
-                "<｜tool▁call▁end｜>",
-                "<｜tool▁calls▁end｜>",
-            };
-        });
-    }
-    return data;
-}
-
 static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) {
     builder.try_parse_reasoning("<think>", "</think>");
     if (!builder.syntax().parse_tool_calls) {
@@ -1479,66 +1333,6 @@ static void common_chat_parse_deepseek_r1(common_chat_msg_parser & builder) {
         tool_calls_end);
 }
 
-static void common_chat_parse_deepseek_v3_1_content(common_chat_msg_parser & builder) {
-    static const common_regex function_regex("(?:<｜tool▁call▁begin｜>)?([^\\n<]+)(?:<｜tool▁sep｜>)");
-
-    static const common_regex close_regex("(?:[\\s]*)?<｜tool▁call▁end｜>");
-    static const common_regex tool_calls_begin("(?:<｜tool▁calls▁begin｜>|<｜tool_calls_begin｜>|<｜tool calls begin｜>|<｜tool\\\\_calls\\\\_begin｜>|<｜tool▁calls｜>)");
-    static const common_regex tool_calls_end("<｜tool▁calls▁end｜>");
-
-    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__);
-
-    parse_json_tool_calls(
-        builder,
-        /* block_open= */ tool_calls_begin,
-        /* function_regex_start_only= */ std::nullopt,
-        function_regex,
-        close_regex,
-        tool_calls_end);
-}
-
-static void common_chat_parse_deepseek_v3_1(common_chat_msg_parser & builder) {
-    // DeepSeek V3.1 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_deepseek_v3_1_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__);
-        // </think><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>NAME\n```json\nJSON\n```<｜tool▁call▁end｜><｜tool▁calls▁end｜>
-        common_chat_parse_deepseek_v3_1_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_deepseek_v3_1_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__);
-            // <｜tool▁call▁begin｜>NAME<｜tool▁sep｜>JSON<｜tool▁call▁end｜>
-            common_chat_parse_deepseek_v3_1_content(builder);
-        }
-    }
-}
-
 static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template & tmpl, const struct templates_params & inputs) {
     common_chat_params data;
     auto prompt = apply(tmpl, inputs);
@@ -1741,12 +1535,10 @@ static void common_chat_parse_gpt_oss(common_chat_msg_parser & builder) {
 static common_chat_params common_chat_params_init_firefunction_v2(const common_chat_template & tmpl, const struct templates_params & inputs) {
     LOG_DBG("%s\n", __func__);
     common_chat_params data;
-    const std::optional<json> tools_override = json();
-    const std::optional<json> additional_context = json {
+    data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, /* tools_override= */ json(), json {
         {"datetime", format_time(inputs.now, "%b %d %Y %H:%M:%S GMT")},
         {"functions", json(inputs.tools.empty() ? "" : inputs.tools.dump(2))},
-    };
-    data.prompt = apply(tmpl, inputs, /* messages_override =*/ std::nullopt, tools_override, additional_context);
+    });
     if (inputs.tools.is_array() && !inputs.tools.empty()) {
         data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
         data.grammar = build_grammar([&](const common_grammar_builder & builder) {
@@ -2037,7 +1829,7 @@ static common_chat_params common_chat_params_init_hermes_2_pro(const common_chat
                 // If thinking_forced_open, then we capture the </think> tag in the grammar,
                 // (important for required tool choice) and in the trigger's first capture (decides what is sent to the grammar)
                 std::string(data.thinking_forced_open ? "[\\s\\S]*?(</think>\\s*)" : "(?:<think>[\\s\\S]*?</think>\\s*)?") + (
-                    "\\s*("
+                    "(\\s*"
                     "(?:<tool_call>"
                     "|<function"
                     "|(?:```(?:json|xml)?\n\\s*)?(?:<function_call>|<tools>|<xml><json>|<response>)?"
@@ -2232,28 +2024,15 @@ static common_chat_params common_chat_params_init_granite(const common_chat_temp
 
 static void common_chat_parse_granite(common_chat_msg_parser & builder) {
     // Parse thinking tags
-    static const common_regex start_think_regex(regex_escape("<think>"));
-    static const common_regex end_think_regex(regex_escape("</think>"));
-    // Granite models output partial tokens such as "<" and "<think".
-    // By leveraging try_consume_regex()/try_find_regex() throwing
-    // common_chat_msg_partial_exception for these partial tokens,
-    // processing is interrupted and the tokens are not passed to add_content().
-    if (auto res = builder.try_consume_regex(start_think_regex)) {
-        // Restore position for try_parse_reasoning()
-        builder.move_to(res->groups[0].begin);
-        builder.try_find_regex(end_think_regex, std::string::npos, false);
-        // Restore position for try_parse_reasoning()
-        builder.move_to(res->groups[0].begin);
-    }
     builder.try_parse_reasoning("<think>", "</think>");
 
-    // Parse response tags
-    static const common_regex start_response_regex(regex_escape("<response>"));
-    static const common_regex end_response_regex(regex_escape("</response>"));
-    // Granite models output partial tokens such as "<" and "<response".
-    // Same hack as reasoning parsing.
-    if (builder.try_consume_regex(start_response_regex)) {
-        builder.try_find_regex(end_response_regex);
+    // Parse response tags using regex
+    static const common_regex response_regex("<response>([\\s\\S]*?)</response>");
+    if (auto res = builder.try_find_regex(response_regex)) {
+        // Extract the content between the tags (capture group 1)
+        auto content = builder.str(res->groups[1]);
+        builder.add_content(content);
+        builder.move_to(res->groups[0].end);
     }
 
     if (!builder.syntax().parse_tool_calls) {
@@ -2267,131 +2046,19 @@ static void common_chat_parse_granite(common_chat_msg_parser & builder) {
         builder.move_to(res->groups[0].end);
 
         // Expect JSON array of tool calls
-        if (auto tool_call = builder.try_consume_json_with_dumped_args({{{"arguments"}}})) {
-            if (!builder.add_tool_calls(tool_call->value) || tool_call->is_partial) {
-                throw common_chat_msg_partial_exception("incomplete tool call");
+        auto tool_calls_data = builder.consume_json();
+        if (tool_calls_data.json.is_array()) {
+            if (!builder.add_tool_calls(tool_calls_data.json)) {
+                builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
             }
+        } else {
+            builder.add_content("<|tool_call|>" + tool_calls_data.json.dump());
         }
     } else {
         builder.add_content(builder.consume_rest());
     }
 }
 
-static void common_chat_parse_nemotron_v2(common_chat_msg_parser & builder) {
-    // Parse thinking tags
-    builder.try_parse_reasoning("<think>", "</think>");
-    if (!builder.syntax().parse_tool_calls) {
-        builder.add_content(builder.consume_rest());
-        return;
-    }
-
-    // Look for tool calls
-    static const common_regex tool_call_regex(regex_escape("<TOOLCALL>"));
-    if (auto res = builder.try_find_regex(tool_call_regex)) {
-        builder.move_to(res->groups[0].end);
-
-        // Expect JSON array of tool calls
-        auto tool_calls_data = builder.consume_json();
-        if (tool_calls_data.json.is_array()) {
-            if (!builder.try_consume_literal("</TOOLCALL>")) {
-                throw common_chat_msg_partial_exception("Incomplete tool call");
-            }
-            builder.add_tool_calls(tool_calls_data.json);
-        } else {
-            throw common_chat_msg_partial_exception("Incomplete tool call");
-        }
-    }
-    builder.add_content(builder.consume_rest());
-}
-
-static void common_chat_parse_seed_oss(common_chat_msg_parser & builder) {
-    // Parse thinking tags first - this handles the main reasoning content
-    builder.try_parse_reasoning("<seed:think>", "</seed:think>");
-
-    if (!builder.syntax().parse_tool_calls) {
-        builder.add_content(builder.consume_rest());
-        return;
-    }
-
-    // Parse tool calls - Seed-OSS uses <seed:tool_call> format
-    static const common_regex tool_call_begin_regex("<seed:tool_call>");
-    static const common_regex tool_call_end_regex("</seed:tool_call>");
-    static const common_regex function_regex("<function=([^>]+)>");
-    static const common_regex param_regex("<parameter=([^>]+)>");
-
-    while (auto tool_res = builder.try_find_regex(tool_call_begin_regex)) {
-        builder.consume_spaces();  // Consume whitespace after <seed:tool_call>
-
-        // Look for function call inside tool call, ignore any content before it
-        if (auto func_res = builder.try_find_regex(function_regex, std::string::npos, false)) {
-            auto function_name = builder.str(func_res->groups[1]);
-
-            // Parse Seed-OSS parameters <parameter=name>value</parameter>
-            json args = json::object();
-            // Parse all parameters
-            while (auto param_res = builder.try_find_regex(param_regex, std::string::npos, false)) {
-                // again, ignore noise around parameters
-                auto param_name = builder.str(param_res->groups[1]);
-                builder.move_to(param_res->groups[0].end);
-                builder.consume_spaces();  // Consume whitespace after parameter
-                auto savedPos = builder.pos();
-                if (auto param_parse = builder.try_find_literal("</parameter>")) {
-                    auto param = param_parse->prelude;
-                    builder.move_to(savedPos);
-                    try {
-                        if (auto param_res = builder.try_consume_json()) {
-                            args[param_name] = param_res->json;
-                        } else {
-                            args[param_name] = param;
-                        }
-                    } catch (json::exception &) {
-                        args[param_name] = param;
-                    }
-                } else {
-                    throw common_chat_msg_partial_exception("Incomplete tool parameter");
-                }
-            }
-            // Look for closing function tag
-            auto end_func = builder.try_find_literal("</function>");
-            if (end_func) {
-                builder.move_to(end_func->groups[0].end);
-                builder.consume_spaces();  // Consume whitespace after </function>
-
-                // Add the tool call with parsed arguments, but only if we REALLY got the literal
-                auto eaten_fragment = builder.input().substr(end_func->groups[0].begin, end_func->groups[0].end);
-                auto funlen = std::string("</function>").length();
-                if (eaten_fragment.length() >= funlen && eaten_fragment.substr(0, funlen) == std::string("</function>")) {
-                    if (!builder.add_tool_call(function_name, "", args.dump())) {
-                        throw common_chat_msg_partial_exception("Incomplete tool call");
-                    }
-                } else {
-                    throw common_chat_msg_partial_exception("Incomplete tool call");
-                }
-            } else {
-                throw common_chat_msg_partial_exception("Incomplete tool call");
-            }
-            // Look for closing tool call tag
-            if (auto end_tool = builder.try_find_regex(tool_call_end_regex, std::string::npos, false)) {
-                builder.move_to(end_tool->groups[0].end);
-                builder.consume_spaces();  // Consume trailing whitespace after tool call
-            } else {
-                throw common_chat_msg_partial_exception("Incomplete tool call");
-            }
-        } else {
-            // No function found - don't consume content here, let it be handled at the end
-            break;
-        }
-    }
-
-    // Consume any remaining whitespace after all tool call processing
-    builder.consume_spaces();
-    auto remaining = builder.consume_rest();
-    // If there's any non-whitespace content remaining, add it as content
-    if (!string_strip(remaining).empty()) {
-        builder.add_content(remaining);
-    }
-}
-
 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);
@@ -2408,62 +2075,8 @@ static common_chat_params common_chat_params_init_without_tools(const common_cha
     return data;
 }
 
-static common_chat_params common_chat_params_init_seed_oss(
-    const common_chat_template         & tmpl,
-    templates_params                   & params,
-    const common_chat_templates_inputs & inputs)
-{
-    common_chat_params data;
-    data.prompt = apply(tmpl, params);
-    data.format = COMMON_CHAT_FORMAT_SEED_OSS;
-    if (string_ends_with(data.prompt, "<seed:think>")) {
-        if (!inputs.enable_thinking) {
-            data.prompt += "</seed:think>";
-        } else {
-            data.thinking_forced_open = true;
-        }
-    }
-
-    if (params.tools.is_array() && !params.tools.empty()) {
-        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
-            std::vector<std::string> tool_rules;
-            foreach_function(params.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);
-
-                // Create rule for Seed-OSS function call format
-                std::string param_rules;
-                if (parameters.contains("properties")) {
-                    for (const auto & [key, value] : parameters.at("properties").items()) {
-                        param_rules += "\"<parameter=" + key + ">\"" + builder.add_schema(name + "-arg-" + key, value) +
-                                       "\"</parameter>\"";
-                    }
-                }
-
-                tool_rules.push_back(builder.add_rule(name + "-call",
-                                                      "\"<seed:tool_call>\" space \"<function=" + name + ">\" space " +
-                                                          param_rules +
-                                                          " \"</function>\" space \"</seed:tool_call>\""));
-            });
-
-            data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<seed:tool_call>" });
-
-            data.preserved_tokens = {
-                "<seed:think>", "</seed:think>", "<seed:tool_call>", "</seed:tool_call>",
-                "<function=",   "</function>",   "<parameter=",      "</parameter>",
-            };
-
-            builder.add_rule("root", string_join(tool_rules, " | "));
-        });
-    }
-    return data;
-}
-
 static common_chat_params common_chat_templates_apply_jinja(
-    const struct common_chat_templates        * tmpls,
+    const struct common_chat_templates * tmpls,
     const struct common_chat_templates_inputs & inputs)
 {
     templates_params params;
@@ -2507,12 +2120,6 @@ static common_chat_params common_chat_templates_apply_jinja(
         }
     }
 
-    // DeepSeek V3.1: detect based on specific patterns in the template
-    if (src.find("message['prefix'] is defined and message['prefix'] and thinking") != std::string::npos &&
-        params.json_schema.is_null()) {
-        return common_chat_params_init_deepseek_v3_1(tmpl, params);
-    }
-
     // DeepSeek R1: use handler in all cases except json schema (thinking / tools).
     if (src.find("<｜tool▁calls▁begin｜>") != std::string::npos && params.json_schema.is_null()) {
         return common_chat_params_init_deepseek_r1(tmpl, params);
@@ -2538,16 +2145,6 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_gpt_oss(tmpl, params);
     }
 
-    // Seed-OSS
-    if (src.find("<seed:think>") != std::string::npos) {
-        return common_chat_params_init_seed_oss(tmpl, params, inputs);
-    }
-
-    // Nemotron v2
-    if (src.find("<SPECIAL_10>") != std::string::npos) {
-        return common_chat_params_init_nemotron_v2(tmpl, params);
-    }
-
     // Use generic handler when mixing tools + JSON schema.
     // TODO: support that mix in handlers below.
     if ((params.tools.is_array() && params.json_schema.is_object())) {
@@ -2685,9 +2282,6 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_DEEPSEEK_R1:
             common_chat_parse_deepseek_r1(builder);
             break;
-        case COMMON_CHAT_FORMAT_DEEPSEEK_V3_1:
-            common_chat_parse_deepseek_v3_1(builder);
-            break;
         case COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2:
             common_chat_parse_functionary_v3_2(builder);
             break;
@@ -2709,12 +2303,6 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
         case COMMON_CHAT_FORMAT_GPT_OSS:
             common_chat_parse_gpt_oss(builder);
             break;
-        case COMMON_CHAT_FORMAT_SEED_OSS:
-            common_chat_parse_seed_oss(builder);
-            break;
-        case COMMON_CHAT_FORMAT_NEMOTRON_V2:
-            common_chat_parse_nemotron_v2(builder);
-            break;
         default:
             throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
     }

common/chat.h

@@ -107,13 +107,10 @@ enum common_chat_format {
     COMMON_CHAT_FORMAT_FIREFUNCTION_V2,
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
     COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
-    COMMON_CHAT_FORMAT_DEEPSEEK_V3_1,
     COMMON_CHAT_FORMAT_HERMES_2_PRO,
     COMMON_CHAT_FORMAT_COMMAND_R7B,
     COMMON_CHAT_FORMAT_GRANITE,
     COMMON_CHAT_FORMAT_GPT_OSS,
-    COMMON_CHAT_FORMAT_SEED_OSS,
-    COMMON_CHAT_FORMAT_NEMOTRON_V2,
 
     COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
 };
@@ -200,8 +197,6 @@ common_chat_msg           common_chat_parse(const std::string & input, bool is_p
 
 common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice);
 
-bool common_chat_templates_support_enable_thinking(const common_chat_templates * chat_templates);
-
 // Parses a JSON array of messages in OpenAI's chat completion API format.
 // T can be std::string containing JSON or nlohmann::ordered_json
 template <class T> std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const T & messages);

common/common.cpp

@@ -901,8 +901,7 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
     if (model == NULL) {
-        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
         return iparams;
     }
 
@@ -912,8 +911,7 @@ struct common_init_result common_init_from_params(common_params & params) {
 
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
-        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
         llama_model_free(model);
         return iparams;
     }
@@ -990,12 +988,7 @@ struct common_init_result common_init_from_params(common_params & params) {
             return iparams;
         }
 
-        char buf[1024];
         la.ptr = lora.get();
-        llama_adapter_meta_val_str(la.ptr, "adapter.lora.task_name", buf, sizeof(buf));
-        la.task_name = buf;
-        llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
-        la.prompt_prefix = buf;
         iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
     }
 
@@ -1159,10 +1152,10 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.yarn_orig_ctx     = params.yarn_orig_ctx;
     cparams.pooling_type      = params.pooling_type;
     cparams.attention_type    = params.attention_type;
-    cparams.flash_attn_type   = params.flash_attn_type;
     cparams.cb_eval           = params.cb_eval;
     cparams.cb_eval_user_data = params.cb_eval_user_data;
     cparams.offload_kqv       = !params.no_kv_offload;
+    cparams.flash_attn        = params.flash_attn;
     cparams.no_perf           = params.no_perf;
     cparams.op_offload        = !params.no_op_offload;
     cparams.swa_full          = params.swa_full;

common/common.h

@@ -34,9 +34,6 @@ struct common_adapter_lora_info {
     std::string path;
     float scale;
 
-    std::string task_name;
-    std::string prompt_prefix;
-
     struct llama_adapter_lora * ptr;
 };
 
@@ -193,11 +190,10 @@ struct common_params_sampling {
 };
 
 struct common_params_model {
-    std::string path        = ""; // model local path                                       // NOLINT
-    std::string url         = ""; // model url to download                                  // NOLINT
-    std::string hf_repo     = ""; // HF repo                                                // NOLINT
-    std::string hf_file     = ""; // HF file                                                // NOLINT
-    std::string docker_repo = ""; // Docker repo                                            // NOLINT
+    std::string path    = ""; // model local path                                           // NOLINT
+    std::string url     = ""; // model url to download                                      // NOLINT
+    std::string hf_repo = ""; // HF repo                                                    // NOLINT
+    std::string hf_file = ""; // HF file                                                    // NOLINT
 };
 
 struct common_params_speculative {
@@ -288,9 +284,9 @@ struct common_params {
     float   rope_freq_base        =  0.0f; // RoPE base frequency
     float   rope_freq_scale       =  0.0f; // RoPE frequency scaling factor
     float   yarn_ext_factor       = -1.0f; // YaRN extrapolation mix factor
-    float   yarn_attn_factor      = -1.0f; // YaRN magnitude scaling factor
-    float   yarn_beta_fast        = -1.0f; // YaRN low correction dim
-    float   yarn_beta_slow        = -1.0f; // YaRN high correction dim
+    float   yarn_attn_factor      =  1.0f; // YaRN magnitude scaling factor
+    float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
+    float   yarn_beta_slow        =  1.0f; // YaRN high correction dim
     int32_t yarn_orig_ctx         =     0; // YaRN original context length
 
     // offload params
@@ -313,7 +309,6 @@ struct common_params {
     enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
     enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
     enum llama_attention_type    attention_type    = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
-    enum llama_flash_attn_type   flash_attn_type   = LLAMA_FLASH_ATTN_TYPE_AUTO; // whether to use Flash Attention
 
     struct common_params_sampling    sampling;
     struct common_params_speculative speculative;
@@ -377,6 +372,7 @@ struct common_params {
     bool multiline_input   = false; // reverse the usage of `\`
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
+    bool flash_attn        = false; // flash attention
     bool no_perf           = false; // disable performance metrics
     bool ctx_shift         = false;  // context shift on infinite text generation
     bool swa_full          = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
@@ -445,7 +441,7 @@ struct common_params {
 
     // "advanced" endpoints are disabled by default for better security
     bool webui            = true;
-    bool endpoint_slots   = true;
+    bool endpoint_slots   = false;
     bool endpoint_props   = false; // only control POST requests, not GET
     bool endpoint_metrics = false;
 
@@ -453,7 +449,7 @@ struct common_params {
 
     std::string slot_save_path;
 
-    float slot_prompt_similarity = 0.1f;
+    float slot_prompt_similarity = 0.5f;
 
     // batched-bench params
     bool is_pp_shared = false;
@@ -734,20 +730,6 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
 
 }
 
-//
-// MoE utils
-//
-
-const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_exps";
-
-static std::string llm_ffn_exps_block_regex(int idx) {
-    return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);
-}
-
-static llama_model_tensor_buft_override llm_ffn_exps_cpu_override() {
-    return { LLM_FFN_EXPS_REGEX, ggml_backend_cpu_buffer_type() };
-}
-
 //
 // training utils
 //

common/json-schema-to-grammar.cpp

@@ -257,13 +257,12 @@ std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
 };
 
 static bool is_reserved_name(const std::string & name) {
-    static const std::unordered_set<std::string> RESERVED_NAMES = [] {
-        std::unordered_set<std::string> s;
-        s.insert("root");
-        for (const auto & p : PRIMITIVE_RULES) s.insert(p.first);
-        for (const auto & p : STRING_FORMAT_RULES) s.insert(p.first);
-        return s;
-    }();
+    static std::unordered_set<std::string> RESERVED_NAMES;
+    if (RESERVED_NAMES.empty()) {
+        RESERVED_NAMES.insert("root");
+        for (const auto &p : PRIMITIVE_RULES) RESERVED_NAMES.insert(p.first);
+        for (const auto &p : STRING_FORMAT_RULES) RESERVED_NAMES.insert(p.first);
+    }
     return RESERVED_NAMES.find(name) != RESERVED_NAMES.end();
 }
 
@@ -844,10 +843,9 @@ public:
                 _build_object_rule(
                     properties, required, name,
                     schema.contains("additionalProperties") ? schema["additionalProperties"] : json()));
-        } else if ((schema_type.is_null() || schema_type == "object" || schema_type == "string") && schema.contains("allOf")) {
+        } else if ((schema_type.is_null() || schema_type == "object") && schema.contains("allOf")) {
             std::unordered_set<std::string> required;
             std::vector<std::pair<std::string, json>> properties;
-            std::map<std::string, size_t> enum_values;
             std::string hybrid_name = name;
             std::function<void(const json &, bool)> add_component = [&](const json & comp_schema, bool is_required) {
                 if (comp_schema.contains("$ref")) {
@@ -859,14 +857,6 @@ public:
                             required.insert(prop.key());
                         }
                     }
-                } else if (comp_schema.contains("enum")) {
-                    for (const auto & v : comp_schema["enum"]) {
-                        const auto rule = _generate_constant_rule(v);
-                        if (enum_values.find(rule) == enum_values.end()) {
-                            enum_values[rule] = 0;
-                        }
-                        enum_values[rule] += 1;
-                    }
                 } else {
                   // todo warning
                 }
@@ -880,17 +870,6 @@ public:
                     add_component(t, true);
                 }
             }
-            if (!enum_values.empty()) {
-                std::vector<std::string> enum_intersection;
-                for (const auto & p : enum_values) {
-                    if (p.second == schema["allOf"].size()) {
-                        enum_intersection.push_back(p.first);
-                    }
-                }
-                if (!enum_intersection.empty()) {
-                    return _add_rule(rule_name, "(" + string_join(enum_intersection, " | ") + ") space");
-                }
-            }
             return _add_rule(rule_name, _build_object_rule(properties, required, hybrid_name, json()));
         } else if ((schema_type.is_null() || schema_type == "array") && (schema.contains("items") || schema.contains("prefixItems"))) {
             json items = schema.contains("items") ? schema["items"] : schema["prefixItems"];

common/log.cpp

@@ -4,52 +4,17 @@
 #include <condition_variable>
 #include <cstdarg>
 #include <cstdio>
-#include <cstdlib>
-#include <cstring>
 #include <mutex>
 #include <sstream>
 #include <thread>
 #include <vector>
 
-#if defined(_WIN32)
-#    include <io.h>
-#    include <windows.h>
-#    define isatty _isatty
-#    define fileno _fileno
-#else
-#    include <unistd.h>
-#endif // defined(_WIN32)
-
 int common_log_verbosity_thold = LOG_DEFAULT_LLAMA;
 
 void common_log_set_verbosity_thold(int verbosity) {
     common_log_verbosity_thold = verbosity;
 }
 
-// Auto-detect if colors should be enabled based on terminal and environment
-static bool common_log_should_use_colors_auto() {
-    // Check NO_COLOR environment variable (https://no-color.org/)
-    if (const char * no_color = std::getenv("NO_COLOR")) {
-        if (no_color[0] != '\0') {
-            return false;
-        }
-    }
-
-    // Check TERM environment variable
-    if (const char * term = std::getenv("TERM")) {
-        if (std::strcmp(term, "dumb") == 0) {
-            return false;
-        }
-    }
-
-    // Check if stdout and stderr are connected to a terminal
-    // We check both because log messages can go to either
-    bool stdout_is_tty = isatty(fileno(stdout));
-    bool stderr_is_tty = isatty(fileno(stderr));
-
-    return stdout_is_tty || stderr_is_tty;
-}
-
 static int64_t t_us() {
     return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
 }
@@ -388,11 +353,6 @@ struct common_log * common_log_init() {
 
 struct common_log * common_log_main() {
     static struct common_log log;
-    static std::once_flag    init_flag;
-    std::call_once(init_flag, [&]() {
-        // Set default to auto-detect colors
-        log.set_colors(common_log_should_use_colors_auto());
-    });
 
     return &log;
 }
@@ -420,19 +380,8 @@ void common_log_set_file(struct common_log * log, const char * file) {
     log->set_file(file);
 }
 
-void common_log_set_colors(struct common_log * log, log_colors colors) {
-    if (colors == LOG_COLORS_AUTO) {
-        log->set_colors(common_log_should_use_colors_auto());
-        return;
-    }
-
-    if (colors == LOG_COLORS_DISABLED) {
-        log->set_colors(false);
-        return;
-    }
-
-    GGML_ASSERT(colors == LOG_COLORS_ENABLED);
-    log->set_colors(true);
+void common_log_set_colors(struct common_log * log, bool colors) {
+    log->set_colors(colors);
 }
 
 void common_log_set_prefix(struct common_log * log, bool prefix) {

common/log.h

@@ -24,12 +24,6 @@
 #define LOG_DEFAULT_DEBUG 1
 #define LOG_DEFAULT_LLAMA 0
 
-enum log_colors {
-    LOG_COLORS_AUTO     = -1,
-    LOG_COLORS_DISABLED = 0,
-    LOG_COLORS_ENABLED  = 1,
-};
-
 // needed by the LOG_TMPL macro to avoid computing log arguments if the verbosity lower
 // set via common_log_set_verbosity()
 extern int common_log_verbosity_thold;
@@ -71,10 +65,10 @@ void common_log_add(struct common_log * log, enum ggml_log_level level, const ch
 // D - debug   (stderr, V = LOG_DEFAULT_DEBUG)
 //
 
-void common_log_set_file      (struct common_log * log, const char * file); // not thread-safe
-void common_log_set_colors    (struct common_log * log, log_colors colors); // not thread-safe
-void common_log_set_prefix    (struct common_log * log, bool prefix);       // whether to output prefix to each log
-void common_log_set_timestamps(struct common_log * log, bool timestamps);   // whether to output timestamps in the prefix
+void common_log_set_file      (struct common_log * log, const char * file);       // not thread-safe
+void common_log_set_colors    (struct common_log * log,       bool   colors);     // not thread-safe
+void common_log_set_prefix    (struct common_log * log,       bool   prefix);     // whether to output prefix to each log
+void common_log_set_timestamps(struct common_log * log,       bool   timestamps); // whether to output timestamps in the prefix
 
 // helper macros for logging
 // use these to avoid computing log arguments if the verbosity of the log is higher than the threshold

common/sampling.cpp

@@ -426,29 +426,8 @@ uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
 
 // helpers
 
-llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl, bool do_sort) {
-    auto * res = &gsmpl->cur_p;
-
-    if (do_sort && !res->sorted) {
-        // remember the selected token before sorting
-        const llama_token id = res->data[res->selected].id;
-
-        std::sort(res->data, res->data + res->size, [](const llama_token_data & a, const llama_token_data & b) {
-            return a.p > b.p;
-        });
-
-        // restore the selected token after sorting
-        for (size_t i = 0; i < res->size; ++i) {
-            if (res->data[i].id == id) {
-                res->selected = i;
-                break;
-            }
-        }
-
-        res->sorted = true;
-    }
-
-    return res;
+llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl) {
+    return &gsmpl->cur_p;
 }
 
 llama_token common_sampler_last(const struct common_sampler * gsmpl) {

common/sampling.h

@@ -86,9 +86,7 @@ uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
 // helpers
 
 // access the internal list of current candidate tokens
-// if do_sort == true, the candidates are guaranteed to be sorted afterwards (in descending order of probability)
-// the .sorted flag of the result indicates whether the returned candidates are sorted
-llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl, bool do_sort);
+llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl);
 
 // get the last accepted token
 llama_token common_sampler_last(const struct common_sampler * gsmpl);

common/speculative.cpp

@@ -317,7 +317,7 @@ llama_tokens common_speculative_gen_draft(
 
         common_sampler_sample(smpl, ctx_dft, 0, true);
 
-        const auto * cur_p = common_sampler_get_candidates(smpl, true);
+        const auto * cur_p = common_sampler_get_candidates(smpl);
 
         for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
             LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",

convert_hf_to_gguf.py

@@ -72,7 +72,6 @@ class ModelBase:
     endianess: gguf.GGUFEndian
     use_temp_file: bool
     lazy: bool
-    dry_run: bool
     part_names: list[str]
     is_safetensors: bool
     hparams: dict[str, Any]
@@ -112,7 +111,6 @@ class ModelBase:
         self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE
         self.use_temp_file = use_temp_file
         self.lazy = not eager or (remote_hf_model_id is not None)
-        self.dry_run = dry_run
         self.remote_hf_model_id = remote_hf_model_id
         if remote_hf_model_id is not None:
             self.is_safetensors = True
@@ -302,6 +300,10 @@ class ModelBase:
                 # data = data_torch.squeeze().numpy()
                 data = data_torch.numpy()
 
+                # if data ends up empty, it means data_torch was a scalar tensor -> restore
+                if len(data.shape) == 0:
+                    data = data_torch.numpy()
+
                 n_dims = len(data.shape)
                 data_qtype: gguf.GGMLQuantizationType | bool = self.tensor_force_quant(name, new_name, bid, n_dims)
 
@@ -735,9 +737,6 @@ class TextModel(ModelBase):
         if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
             # ref: https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
             res = "qwen2"
-        if chkhsh == "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273":
-            # ref: https://huggingface.co/alvarobartt/grok-2-tokenizer
-            res = "grok-2"
         if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
             # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
             res = "llama-bpe"
@@ -888,9 +887,6 @@ class TextModel(ModelBase):
         if chkhsh == "a1e163ecab2e718a4c829d1148b6e86824ec36163bb71941c3dca9cd5ac25756":
             # ref: https://huggingface.co/JetBrains/Mellum-4b-base
             res = "mellum"
-        if chkhsh == "9b1be57e70d20d9501b2b3186e792d81181ae36ada3903c26f9fea418cf87206":
-            # ref: https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Base
-            res = "llada-moe"
 
         if res is None:
             logger.warning("\n")
@@ -2393,10 +2389,7 @@ class SmolVLMModel(MmprojModel):
         return [] # skip other tensors
 
 
-@ModelBase.register(
-    "Llama4ForConditionalGeneration",
-    "Llama4ForCausalLM",
-)
+@ModelBase.register("Llama4ForConditionalGeneration")
 class Llama4Model(LlamaModel):
     model_arch = gguf.MODEL_ARCH.LLAMA4
     undo_permute = False
@@ -2414,10 +2407,6 @@ class Llama4Model(LlamaModel):
         super().set_gguf_parameters()
         self.gguf_writer.add_interleave_moe_layer_step(self.hparams["interleave_moe_layer_step"])
         self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size_moe"])
-        if "layer_types" in self.hparams:
-            if all(lt == "full_attention" for lt in self.hparams["layer_types"]):
-                # all layers are full attention (for MobileLLM), disable swa
-                self.gguf_writer.add_sliding_window(0)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
         if name.startswith("language_model."):
@@ -2695,20 +2684,12 @@ class BitnetModel(TextModel):
         yield (new_name, data_torch)
 
 
-@ModelBase.register("GrokForCausalLM", "Grok1ForCausalLM")
+@ModelBase.register("GrokForCausalLM")
 class GrokModel(TextModel):
     model_arch = gguf.MODEL_ARCH.GROK
 
     def set_vocab(self):
-        if (self.dir_model / 'tokenizer.model').is_file():
-            self._set_vocab_sentencepiece()
-            return
-
-        if not (self.dir_model / 'tokenizer.json').is_file() or not (self.dir_model / 'chat_template.jinja').is_file():
-            logger.error('Error: Missing vocab and chat template, download files from https://huggingface.co/alvarobartt/grok-2-tokenizer')
-            sys.exit(1)
-
-        self._set_vocab_gpt2()
+        self._set_vocab_sentencepiece()
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -2716,46 +2697,11 @@ class GrokModel(TextModel):
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
-        self.gguf_writer.add_attn_logit_softcapping(self.hparams.get("attn_logit_softcapping", 30.0))
-        self.gguf_writer.add_router_logit_softcapping(self.hparams.get("router_logit_softcapping", 30.0))
-        if (final_logit_softcap := self.hparams.get("final_logit_softcapping")):
-            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
-
-        if (rope_dim := self.hparams.get("head_dim")) is None:
-            rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-
-        if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
-
-        # Treat "original" as "yarn", seems to have been a mistake
-        if self.hparams.get("rope_type") in ("yarn", "original"):
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(self.hparams["scaling_factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["original_max_position_embeddings"])
-            self.gguf_writer.add_rope_scaling_yarn_ext_factor(self.hparams["extrapolation_factor"])
-            self.gguf_writer.add_rope_scaling_yarn_attn_factor(self.hparams["attn_factor"])
-            self.gguf_writer.add_rope_scaling_yarn_beta_fast(self.hparams["beta_fast"])
-            self.gguf_writer.add_rope_scaling_yarn_beta_slow(self.hparams["beta_slow"])
-
-        if temp_len := self.hparams.get("attn_temperature_len"):
-            self.gguf_writer.add_attn_temperature_length(temp_len)
-
-        self.gguf_writer.add_attn_output_scale(self.hparams.get("attn_output_multiplier", rope_dim**-0.5))
-        self.gguf_writer.add_embedding_scale(self.hparams["embedding_multiplier_scale"])
-        self.gguf_writer.add_logit_scale(self.hparams["output_multiplier_scale"])
-
-    _experts: list[dict[str, list[Tensor]]] | None = None
-    _cur_expert = ""
+    _experts: list[dict[str, Tensor]] | None = None
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        tensors: list[tuple[str, Tensor]] = []
-        is_expert = ".moe." in name or ".block_sparse_moe.experts." in name
-
-        if not is_expert:
-            tensors.append((self.map_tensor_name(name), data_torch))
-
         # process the experts separately
-        if is_expert or self._cur_expert:
+        if name.find(".moe.") != -1:
             n_experts = self.hparams["num_local_experts"]
 
             assert bid is not None
@@ -2763,41 +2709,32 @@ class GrokModel(TextModel):
             if self._experts is None:
                 self._experts = [{} for _ in range(self.block_count)]
 
-            # concatenate split tensors
-            if name in self._experts[bid]:
-                self._cur_expert = name
-                self._experts[bid][name].append(data_torch)
-                return []
-            elif is_expert:
-                self._cur_expert = name
-                self._experts[bid][name] = [data_torch]
-                return []
+            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 wid in ["linear", "linear_1", "linear_v"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    tensors.append((new_name, data_torch))
+                return tensors
             else:
-                self._cur_expert = ""
+                return []
 
-            for bid in range(self.block_count):
-                if len(self._experts[bid]) >= n_experts * 3:
-                    # merge the experts into a single 3d tensor
-                    for wid in [("linear", "w1", 0), ("linear_1", "w2", 1), ("linear_v", "w3", 0)]:
-                        datas: list[Tensor] = []
-
-                        for xid in range(n_experts):
-                            ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid[0]}.weight"
-                            if ename not in self._experts[bid]:
-                                ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid[1]}.weight"
-                            tensor_list = self._experts[bid][ename]
-                            datas.append(torch.cat(tensor_list, dim=wid[2]) if len(tensor_list) > 1 else tensor_list[0])
-                            del self._experts[bid][ename]
-
-                        data_torch = torch.stack(datas, dim=0)
-
-                        merged_name = f"transformer.decoder_layer.{bid}.moe.{wid[0]}.weight"
-
-                        new_name = self.map_tensor_name(merged_name)
-
-                        yield (new_name, data_torch)
-
-        yield from tensors
+        return [(self.map_tensor_name(name), data_torch)]
 
 
 @ModelBase.register("DbrxForCausalLM")
@@ -4934,35 +4871,11 @@ class NeoBert(BertModel):
 @ModelBase.register("XLMRobertaModel", "XLMRobertaForSequenceClassification")
 class XLMRobertaModel(BertModel):
     model_arch = gguf.MODEL_ARCH.BERT
-    _lora_files = {}
-    _lora_names = []
 
-    def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, **kwargs: Any):
-        hparams = kwargs.pop("hparams", None)
-        if hparams is None:
-            hparams = ModelBase.load_hparams(dir_model, False)
-
-        if lora_names := hparams.get("lora_adaptations"):
-            self._lora_names = lora_names
-            self.model_arch = gguf.MODEL_ARCH.JINA_BERT_V3
-
-        super().__init__(dir_model, ftype, fname_out, hparams=hparams, **kwargs)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
         self._xlmroberta_tokenizer_init()
 
-    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if self._lora_names:
-            for name in self._lora_names:
-                fname = self.add_prefix_to_filename(self.fname_out, f"lora-{name}-")
-                self._lora_files[name] = gguf.GGUFWriter(fname, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file, dry_run=self.dry_run)
-
-        return super().generate_extra_tensors()
-
-    def set_type(self):
-        for lora_writer in self._lora_files.values():
-            lora_writer.add_type(gguf.GGUFType.ADAPTER)
-            lora_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
-        super().set_type()
-
     def set_vocab(self):
         self._xlmroberta_set_vocab()
 
@@ -4972,62 +4885,13 @@ class XLMRobertaModel(BertModel):
         if name.startswith("roberta."):
             name = name[8:]
 
-        # jina-embeddings-v3
-        if ".parametrizations." in name:
-            name = name.replace(".parametrizations.", ".")
-            if name.endswith(".original"):
-                name = name[:-9]
-
         # position embeddings start at pad_token_id + 1, so just chop down the weight tensor
         if name == "embeddings.position_embeddings.weight":
             if self._position_offset is not None:
                 data_torch = data_torch[self._position_offset:,:]
 
-        if name.endswith(".0.lora_A") or name.endswith(".0.lora_B"):
-            if name.startswith("pooler.dense"):
-                return []
-
-            num_loras = data_torch.size(0)
-            assert num_loras == len(self._lora_names)
-
-            # Split out each LoRA in their own GGUF
-            for i, lora_writer in enumerate(self._lora_files.values()):
-                new_name = self.map_tensor_name(name[:-9]) + name[-7:].lower()
-                data = data_torch[i, :, :]
-                # Transpose/flip token_embd/types into correct shape
-                if new_name == "token_embd.weight.lora_b":
-                    data = data.T
-                elif new_name.startswith("token_types.weight."):
-                    new_name = new_name[:-1] + ("a" if new_name[-1:] == "b" else "b")
-                lora_writer.add_tensor(new_name, data.float().numpy(), raw_dtype=gguf.GGMLQuantizationType.F32)
-
-            return []
-
         return super().modify_tensors(data_torch, name, bid)
 
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # jina-embeddings-v3
-        if rotary_emb_base := self.hparams.get("rotary_emb_base"):
-            self.gguf_writer.add_rope_freq_base(rotary_emb_base)
-        lora_alpha = self.hparams.get("lora_alpha")
-        if lora_prompt_prefixes := self.hparams.get("task_instructions"):
-            assert self._lora_files and all(lora_name in lora_prompt_prefixes for lora_name in self._lora_files.keys())
-        for lora_name, lora_writer in self._lora_files.items():
-            lora_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, lora_alpha if lora_alpha is not None else 1.0)
-            lora_writer.add_string(gguf.Keys.Adapter.LORA_TASK_NAME, lora_name)
-            if lora_prompt_prefixes:
-                lora_writer.add_string(gguf.Keys.Adapter.LORA_PROMPT_PREFIX, lora_prompt_prefixes[lora_name])
-
-    def write(self):
-        super().write()
-        for lora_writer in self._lora_files.values():
-            lora_writer.write_header_to_file()
-            lora_writer.write_kv_data_to_file()
-            lora_writer.write_tensors_to_file(progress=True)
-            lora_writer.close()
-
 
 @ModelBase.register("GemmaForCausalLM")
 class GemmaModel(TextModel):
@@ -5187,29 +5051,6 @@ class Gemma3Model(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
 
-@ModelBase.register("Gemma3TextModel")
-class EmbeddingGemma(Gemma3Model):
-    model_arch = gguf.MODEL_ARCH.GEMMA_EMBEDDING
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        # Override the sliding window size as it gets adjusted by the Gemma3TextConfig
-        # constructor. We want to use the value from the original model's config.json.
-        # ref: https://github.com/huggingface/transformers/pull/40700
-        with open(self.dir_model / "config.json", "r", encoding="utf-8") as f:
-            config = json.load(f)
-            orig_sliding_window = config.get("sliding_window")
-            if orig_sliding_window is None:
-                raise ValueError("sliding_window not found in model config - this is required for the model")
-
-            logger.info(f"Using original sliding_window from config: {orig_sliding_window} "
-                        f"instead of {self.hparams['sliding_window']}")
-            self.gguf_writer.add_sliding_window(orig_sliding_window)
-
-        self._try_set_pooling_type()
-
-
 @ModelBase.register("Gemma3ForConditionalGeneration")
 class Gemma3VisionModel(MmprojModel):
     def set_gguf_parameters(self):
@@ -6016,34 +5857,9 @@ class SeedOssModel(TextModel):
 
 
 @ModelBase.register("Olmo2ForCausalLM")
-@ModelBase.register("Olmo3ForCausalLM")
 class Olmo2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.OLMO2
 
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_attn_factors(rope_scaling["attention_factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
-
-        if "sliding_window" in self.hparams:
-            self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
-
-            sliding_window_pattern = []
-            if "layer_types" in self.hparams:
-                sliding_window_pattern = [t == "sliding_attention" for t in self.hparams["layer_types"]]
-            else:
-                # Olmo2 does not use sliding window attention.
-                # Olmo3 defaults to using sliding window for all layers except every 4th.
-                for i in range(self.hparams["num_hidden_layers"]):
-                    sliding_window_pattern.append((i + 1) % 4 != 0)
-
-            self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
-
 
 @ModelBase.register("OlmoeForCausalLM")
 class OlmoeModel(TextModel):
@@ -6791,8 +6607,6 @@ class T5Model(TextModel):
         self.gguf_writer.add_embedding_length(self.hparams["d_model"])
         self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
         self.gguf_writer.add_block_count(self.hparams["num_layers"])
-        if (dec_n_layer := self.hparams.get("num_decoder_layers")) is not None:
-            self.gguf_writer.add_decoder_block_count(dec_n_layer)
         self.gguf_writer.add_head_count(self.hparams["num_heads"])
         self.gguf_writer.add_key_length(self.hparams["d_kv"])
         self.gguf_writer.add_value_length(self.hparams["d_kv"])
@@ -7657,13 +7471,9 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
         ]
 
         # n_group and d_inner are used during reshape_tensors for mamba2
-        # NOTE: Explicitly include hparam prefix prefix for d_model to
-        #   disambiguate with top-level head_dim
-        # NOTE 2: If needed for future models, this can be isolated in a method
-        #   to separate the prefix setting and teh keys used
-        self.d_model = self.find_hparam([f"{self.hparam_prefixes[0]}_head_dim", "hidden_size", "d_model"])
-        self.n_group = self.find_hparam(["n_groups", "num_groups"])
-        self.d_inner = self.find_hparam(["expand", "num_heads"]) * self.d_model
+        self.d_model = self.find_hparam(["hidden_size", "d_model"])
+        self.n_group = self.find_hparam(["n_groups"])
+        self.d_inner = self.find_hparam(["expand"]) * self.d_model
 
     def get_attn_layers(self):
         # Explicit list of layer type names
@@ -7724,12 +7534,12 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
 
         ## Mamba mixer params ##
         self.gguf_writer.add_ssm_conv_kernel(self.find_hparam(["conv_kernel", "d_conv"]))
-        self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state", "state_dim", "ssm_state_size"]))
+        self.gguf_writer.add_ssm_state_size(self.find_hparam(["state_size", "d_state"]))
         self.gguf_writer.add_ssm_group_count(self.n_group)
         self.gguf_writer.add_ssm_inner_size(self.d_inner)
         # NOTE: The mamba_dt_rank is _not_ the right field for how this is used
         #   in llama.cpp
-        self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads", "num_heads"]))
+        self.gguf_writer.add_ssm_time_step_rank(self.find_hparam(["n_heads"]))
 
         ## Attention params ##
         head_count_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"])
@@ -7756,55 +7566,6 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
         Mamba2Model.set_vocab(self)
 
 
-@ModelBase.register("NemotronHForCausalLM")
-class NemotronHModel(GraniteHybridModel):
-    """Hybrid mamba2/attention model from NVIDIA"""
-    model_arch = gguf.MODEL_ARCH.NEMOTRON_H
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # Save the top-level head_dim for later
-        self.head_dim = self.hparams.get("head_dim", self.hparams.get("attention_head_dim"))
-        assert self.head_dim is not None, "Could not find the attention head dim in config"
-
-        # Don't use expand to calculate d_inner
-        self.d_inner = self.find_hparam(["num_heads"]) * self.d_model
-
-        # Update the ssm / attn / mlp layers
-        # M: Mamba2, *: Attention, -: MLP
-        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
-        self._ssm_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "M"]
-        self._mlp_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "-"]
-
-    def get_attn_layers(self):
-        hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
-        assert len(hybrid_override_pattern) == self.block_count, "Mismatch between hybrid override and num_hidden_layers!"
-        return [i for i, val in enumerate(hybrid_override_pattern) if val == "*"]
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-
-        self.gguf_writer.add_key_length(self.head_dim)
-        self.gguf_writer.add_value_length(self.head_dim)
-
-        # Set feed_forward_length
-        # NOTE: This will trigger an override warning. This is preferrable to
-        #   duplicating all the parent logic
-        n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"])
-        self.gguf_writer.add_feed_forward_length([
-            n_ff if i in self._mlp_layers else 0 for i in range(self.block_count)
-        ])
-
-    def set_vocab(self):
-        super().set_vocab()
-
-        # The tokenizer _does_ add a BOS token (via post_processor type
-        # TemplateProcessing) but does not set add_bos_token to true in the
-        # config, so we need to explicitly override it here.
-        self.gguf_writer.add_add_bos_token(True)
-
-
 @ModelBase.register("BailingMoeForCausalLM")
 class BailingMoeModel(TextModel):
     model_arch = gguf.MODEL_ARCH.BAILINGMOE
@@ -8274,76 +8035,6 @@ class HunYuanMoEModel(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
-@ModelBase.register("LLaDAMoEModel", "LLaDAMoEModelLM")
-class LLaDAMoEModel(TextModel):
-    model_arch = gguf.MODEL_ARCH.LLADA_MOE
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        if (n_experts := self.hparams.get("num_experts")) is not None:
-            self.gguf_writer.add_expert_count(n_experts)
-
-        if (expert_intermediate_size := self.hparams.get("expert_intermediate_size")) is not None:
-            self.gguf_writer.add_expert_feed_forward_length(expert_intermediate_size)
-
-        # number of experts used per token (top-k)
-        if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
-            self.gguf_writer.add_expert_used_count(n_experts_used)
-
-        self.gguf_writer.add_mask_token_id(156895)
-        self.gguf_writer.add_causal_attention(False)
-        self.gguf_writer.add_diffusion_shift_logits(False)
-
-    _experts: list[dict[str, Tensor]] | None = None
-
-    # Copied from: Qwen2MoeModel
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        # process the experts separately
-        if name.find("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)]
-
-    # Copied from: Qwen2MoeModel
-    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("HunYuanDenseV1ForCausalLM")
 class HunYuanModel(TextModel):
     model_arch = gguf.MODEL_ARCH.HUNYUAN_DENSE

convert_hf_to_gguf_update.py

@@ -139,7 +139,6 @@ models = [
     {"name": "lfm2",             "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
     {"name": "exaone4",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
     {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
-    {"name": "llada-moe",        "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Base", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -159,7 +158,6 @@ pre_computed_hashes = [
     {"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
     {"name": "kimi-k2",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base",   "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
     {"name": "qwen2",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
-    {"name": "grok-2",    "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
 ]
 
 

convert_lora_to_gguf.py

@@ -12,7 +12,7 @@ import json
 from math import prod
 from pathlib import Path
 from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast
-from transformers import AutoConfig, AutoTokenizer
+from transformers import AutoConfig
 
 import torch
 
@@ -26,8 +26,6 @@ import gguf
 # reuse model definitions from convert_hf_to_gguf.py
 from convert_hf_to_gguf import LazyTorchTensor, ModelBase
 
-from gguf.constants import GGUFValueType
-
 logger = logging.getLogger("lora-to-gguf")
 
 
@@ -371,31 +369,7 @@ if __name__ == '__main__':
                 self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
 
             def set_gguf_parameters(self):
-                logger.debug("GGUF KV: %s = %d", gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
                 self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
-                alora_invocation_tokens = lparams.get("alora_invocation_tokens")
-                invocation_string = lparams.get("invocation_string")
-                if invocation_string and not alora_invocation_tokens:
-                    logger.debug("Tokenizing invocation_string -> alora_invocation_tokens")
-                    base_model_path_or_id = hparams.get("_name_or_path")
-                    try:
-                        tokenizer = AutoTokenizer.from_pretrained(base_model_path_or_id)
-                    except ValueError:
-                        logger.error("Unable to load tokenizer from %s", base_model_path_or_id)
-                        raise
-                    # NOTE: There's an off-by-one with the older aLoRAs where
-                    # the invocation string includes the "<|start_of_turn|>"
-                    # token, but the adapters themselves were trained to
-                    # activate _after_ that first token, so we drop it here.
-                    alora_invocation_tokens = tokenizer(invocation_string)["input_ids"][1:]
-                if alora_invocation_tokens:
-                    logger.debug("GGUF KV: %s = %s", gguf.Keys.Adapter.ALORA_INVOCATION_TOKENS, alora_invocation_tokens)
-                    self.gguf_writer.add_key_value(
-                        gguf.Keys.Adapter.ALORA_INVOCATION_TOKENS,
-                        alora_invocation_tokens,
-                        GGUFValueType.ARRAY,
-                        GGUFValueType.UINT32,
-                    )
 
             def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
                 # Never add extra tensors (e.g. rope_freqs) for LoRA adapters

docs/backend/CANN.md

@@ -293,14 +293,17 @@ We would like to thank Tuo Dai, Shanni Li, and all of the project maintainers fr
 
 ## Environment variable setup
 
+### GGML_CANN_ASYNC_MODE
+
+Enables asynchronous operator submission. Disabled by default.
+
 ### GGML_CANN_MEM_POOL
 
-Specifies the memory pool management strategy, Default is vmm.
+Specifies the memory pool management strategy:
 
 - vmm: Utilizes a virtual memory manager pool. If hardware support for VMM is unavailable, falls back to the legacy (leg) memory pool.
 
 - prio: Employs a priority queue-based memory pool management.
-
 - leg: Uses a fixed-size buffer pool.
 
 ### GGML_CANN_DISABLE_BUF_POOL_CLEAN
@@ -309,16 +312,5 @@ Controls automatic cleanup of the memory pool. This option is only effective whe
 
 ### GGML_CANN_WEIGHT_NZ
 
-Converting the matmul weight format from ND to NZ to improve performance. Enabled by default.
+Converting the matmul weight format from ND to NZ can significantly improve performance on the 310I DUO NPU.
 
-### GGML_CANN_ACL_GRAPH
-
-Operators are executed using ACL graph execution, rather than in op-by-op (eager) mode. Enabled by default.
-
-### GGML_CANN_GRAPH_CACHE_CAPACITY
-
-Maximum number of compiled CANN graphs kept in the LRU cache, default is 12. When the number of cached graphs exceeds this capacity, the least recently used graph will be evicted.
-
-### GGML_CANN_PREFILL_USE_GRAPH
-
-Enable ACL graph execution during the prefill stage, default is false. This option is only effective when FA is enabled.

docs/build-s390x.md

@@ -42,6 +42,18 @@ cmake --build build --config Release -j $(nproc)
     cmake --build build --config Release -j $(nproc)
     ```
 
+-   By default, NNPA is disabled by default. To enable it:
+
+    ```bash
+    cmake -S . -B build             \
+        -DCMAKE_BUILD_TYPE=Release  \
+        -DGGML_BLAS=ON              \
+        -DGGML_BLAS_VENDOR=OpenBLAS \
+        -DGGML_NNPA=ON
+
+    cmake --build build --config Release -j $(nproc)
+    ```
+
 -   For debug builds:
 
     ```bash
@@ -152,11 +164,15 @@ All models need to be converted to Big-Endian. You can achieve this in three cas
 
 Only available in IBM z15/LinuxONE 3 or later system with the `-DGGML_VXE=ON` (turned on by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z14/arch12. In such systems, the APIs can still run but will use a scalar implementation.
 
-### 2. zDNN Accelerator (WIP)
+### 2. NNPA Vector Intrinsics Acceleration
+
+Only available in IBM z16/LinuxONE 4 or later system with the `-DGGML_NNPA=ON` (turned off by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z15/arch13. In such systems, the APIs can still run but will use a scalar implementation.
+
+### 3. zDNN Accelerator (WIP)
 
 Only available in IBM z17/LinuxONE 5 or later system with the `-DGGML_ZDNN=ON` compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z15/arch13. In such systems, the APIs will default back to CPU routines.
 
-### 3. Spyre Accelerator
+### 4. Spyre Accelerator
 
 _Only available with IBM z17 / LinuxONE 5 or later system. No support currently available._
 
@@ -214,6 +230,10 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
     CXXFLAGS="-include cstdint" pip3 install -r requirements.txt
     ```
 
+5. `-DGGML_NNPA=ON` generates gibberish output
+
+    Answer: We are aware of this as detailed in [this issue](https://github.com/ggml-org/llama.cpp/issues/14877). Please either try reducing the number of threads, or disable the compile option using `-DGGML_NNPA=OFF`.
+
 ## Getting Help on IBM Z & LinuxONE
 
 1. **Bugs, Feature Requests**
@@ -238,38 +258,38 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 
 ## Appendix B: SIMD Support Matrix
 
-|            | VX/VXE/VXE2 | zDNN | Spyre |
-|------------|-------------|------|-------|
-| FP32       | ✅           | ✅    | ❓     |
-| FP16       | ✅           | ✅    | ❓     |
-| BF16       | 🚫           | ✅    | ❓     |
-| Q4_0       | ✅           | ❓    | ❓     |
-| Q4_1       | ✅           | ❓    | ❓     |
-| MXFP4      | 🚫           | ❓    | ❓     |
-| Q5_0       | ✅           | ❓    | ❓     |
-| Q5_1       | ✅           | ❓    | ❓     |
-| Q8_0       | ✅           | ❓    | ❓     |
-| Q2_K       | 🚫           | ❓    | ❓     |
-| Q3_K       | ✅           | ❓    | ❓     |
-| Q4_K       | ✅           | ❓    | ❓     |
-| Q5_K       | ✅           | ❓    | ❓     |
-| Q6_K       | ✅           | ❓    | ❓     |
-| TQ1_0      | 🚫           | ❓    | ❓     |
-| TQ2_0      | 🚫           | ❓    | ❓     |
-| IQ2_XXS    | 🚫           | ❓    | ❓     |
-| IQ2_XS     | 🚫           | ❓    | ❓     |
-| IQ2_S      | 🚫           | ❓    | ❓     |
-| IQ3_XXS    | 🚫           | ❓    | ❓     |
-| IQ3_S      | 🚫           | ❓    | ❓     |
-| IQ1_S      | 🚫           | ❓    | ❓     |
-| IQ1_M      | 🚫           | ❓    | ❓     |
-| IQ4_NL     | ✅           | ❓    | ❓     |
-| IQ4_XS     | ✅           | ❓    | ❓     |
-| FP32->FP16 | 🚫           | ❓    | ❓     |
-| FP16->FP32 | 🚫           | ❓    | ❓     |
+|            | VX/VXE/VXE2 | NNPA | zDNN | Spyre |
+| ---------- | ----------- | ---- | ---- | ----- |
+| FP32       | ✅          | ✅   | ✅   | ❓    |
+| FP16       | ✅          | ✅   | ❓   | ❓    |
+| BF16       | 🚫          | 🚫   | ❓   | ❓    |
+| Q4_0       | ✅          | ✅   | ❓   | ❓    |
+| Q4_1       | ✅          | ✅   | ❓   | ❓    |
+| MXFP4      | 🚫          | 🚫   | ❓   | ❓    |
+| Q5_0       | ✅          | ✅   | ❓   | ❓    |
+| Q5_1       | ✅          | ✅   | ❓   | ❓    |
+| Q8_0       | ✅          | ✅   | ❓   | ❓    |
+| Q2_K       | 🚫          | 🚫   | ❓   | ❓    |
+| Q3_K       | ✅          | ✅   | ❓   | ❓    |
+| Q4_K       | ✅          | ✅   | ❓   | ❓    |
+| Q5_K       | ✅          | ✅   | ❓   | ❓    |
+| Q6_K       | ✅          | ✅   | ❓   | ❓    |
+| TQ1_0      | 🚫          | 🚫   | ❓   | ❓    |
+| TQ2_0      | 🚫          | 🚫   | ❓   | ❓    |
+| IQ2_XXS    | 🚫          | 🚫   | ❓   | ❓    |
+| IQ2_XS     | 🚫          | 🚫   | ❓   | ❓    |
+| IQ2_S      | 🚫          | 🚫   | ❓   | ❓    |
+| IQ3_XXS    | 🚫          | 🚫   | ❓   | ❓    |
+| IQ3_S      | 🚫          | 🚫   | ❓   | ❓    |
+| IQ1_S      | 🚫          | 🚫   | ❓   | ❓    |
+| IQ1_M      | 🚫          | 🚫   | ❓   | ❓    |
+| IQ4_NL     | ✅          | ✅   | ❓   | ❓    |
+| IQ4_XS     | ✅          | ✅   | ❓   | ❓    |
+| FP32->FP16 | 🚫          | ✅   | ❓   | ❓    |
+| FP16->FP32 | 🚫          | ✅   | ❓   | ❓    |
 
 -   ✅ - acceleration available
 -   🚫 - acceleration unavailable, will still run using scalar implementation
 -   ❓ - acceleration unknown, please contribute if you can test it yourself
 
-Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on Sep 7, 2025.
+Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on Aug 22, 2025.

docs/build.md

@@ -59,6 +59,8 @@ cmake --build build --config Release
     cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF
     cmake --build build-arm64-windows-llvm-release
     ```
+    Building for arm64 can also be done with the MSVC compiler with the build-arm64-windows-MSVC preset, or the standard CMake build instructions. However, note that the MSVC compiler does not support inline ARM assembly code, used e.g. for the accelerated Q4_0_N_M CPU kernels.
+
     For building with ninja generator and clang compiler as default:
       -set path:set LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\x64;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\lib\x64\uwp;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\x64
       ```bash

docs/function-calling.md

@@ -21,8 +21,6 @@ Function calling is supported for all models (see https://github.com/ggml-org/ll
   - Use `--chat-template-file` to override the template when appropriate (see examples below)
   - Generic support may consume more tokens and be less efficient than a model's native format.
 
-- Multiple/parallel tool calling is supported on some models but disabled by default, enable it by passing `"parallel_tool_calls": true` in the completion endpoint payload.
-
 <details>
 <summary>Show some common templates and which format handler they use</summary>
 

docs/ops.md

@@ -18,7 +18,6 @@ Legend:
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ |
 |                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                           ADD_ID | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
@@ -27,7 +26,6 @@ Legend:
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
 |                          CONV_2D | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ |
 |                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ |
@@ -51,11 +49,9 @@ Legend:
 |                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
 |                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
-|               GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
 |                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
 |                           IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ |
-|                        IM2COL_3D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
 |                              LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
@@ -65,9 +61,7 @@ Legend:
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
 |                             NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
-|                     NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
-|                     OPT_STEP_SGD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
 |                              PAD | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
@@ -104,7 +98,6 @@ Legend:
 |                              SUM | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ |
 |                         SUM_ROWS | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
 |                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
 |                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ |

examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp

@@ -333,17 +333,17 @@ static void print_params(struct my_llama_hparams * params) {
 }
 
 static void print_tensor_info(const struct ggml_context * ctx) {
-    for (auto * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+    for (auto t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
         LOG_INF("%s: Allocating ", __func__);
         int64_t total = 1;
         int i = 0;
         for (; i < ggml_n_dims(t); ++i) {
-            if (i > 0) { LOG_INF("x "); }
-            LOG_INF("[%" PRId64 "] ", t->ne[i]);
+            if (i > 0) LOG("x ");
+            LOG("[%" PRId64 "] ", t->ne[i]);
             total *= t->ne[i];
         }
-        if (i > 1) { LOG_INF("= [%" PRId64 "] ", total); }
-        LOG_INF("float space for %s\n", ggml_get_name(t));
+        if (i > 1) LOG("= [%" PRId64 "] ", total);
+        LOG("float space for %s\n", ggml_get_name(t));
     }
 }
 

examples/diffusion/diffusion-cli.cpp

@@ -510,27 +510,19 @@ static void diffusion_generate(llama_context *          ctx,
     n_generated = params.max_length;
 }
 
-static std::string format_input_text(const std::string & prompt, const std::string & system_prompt, bool use_chat_template, llama_model * model) {
+static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) {
     if (!use_chat_template) {
         return prompt;
     }
 
     auto chat_templates = common_chat_templates_init(model, "");
+
     common_chat_templates_inputs inputs;
-    common_chat_msg system_msg;
-
-    if (!system_prompt.empty()) {
-        system_msg.role = "system";
-        system_msg.content = system_prompt;
-        inputs.messages.push_back(system_msg);
-    }
-
-    common_chat_msg user_msg;
-    user_msg.role = "user";
-    user_msg.content = prompt;
-
-    inputs.messages.push_back(user_msg);
+    common_chat_msg              user_msg;
+    user_msg.role                = "user";
+    user_msg.content             = prompt;
     inputs.add_generation_prompt = true;
+    inputs.messages.push_back(user_msg);
 
     auto result = common_chat_templates_apply(chat_templates.get(), inputs);
 
@@ -572,7 +564,7 @@ int main(int argc, char ** argv) {
     ctx_params.n_ctx                = params.n_ctx;
     ctx_params.n_batch              = params.n_batch;
     ctx_params.n_ubatch             = params.n_ubatch;
-    ctx_params.flash_attn_type      = params.flash_attn_type;
+    ctx_params.flash_attn           = params.flash_attn;
     ctx_params.no_perf              = params.no_perf;
     ctx_params.type_k               = params.cache_type_k;
     ctx_params.type_v               = params.cache_type_v;
@@ -587,8 +579,7 @@ int main(int argc, char ** argv) {
     llama_set_n_threads(ctx, params.cpuparams.n_threads, params.cpuparams_batch.n_threads);
 
     const llama_vocab * vocab            = llama_model_get_vocab(model);
-
-    std::string         formatted_prompt = format_input_text(params.prompt, params.system_prompt, params.enable_chat_template, model);
+    std::string         formatted_prompt = format_input_text(params.prompt, params.enable_chat_template, model);
 
     std::vector<llama_token> input_tokens = common_tokenize(vocab,
                                                             formatted_prompt,
@@ -605,7 +596,6 @@ int main(int argc, char ** argv) {
     }
 
     llama_token mask_token_id = llama_vocab_mask(vocab);
-
     GGML_ASSERT(mask_token_id != LLAMA_TOKEN_NULL);
 
     bool visual_mode = params.diffusion.visual_mode;

examples/eval-callback/eval-callback.cpp

@@ -28,51 +28,9 @@ static std::string ggml_ne_string(const ggml_tensor * t) {
     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(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(*(ggml_fp16_t *) &data[i]);
-    } else if (type == GGML_TYPE_F32) {
-        v = *(float *) &data[i];
-    } else if (type == GGML_TYPE_I64) {
-        v = (float) *(int64_t *) &data[i];
-    } else if (type == GGML_TYPE_I32) {
-        v = (float) *(int32_t *) &data[i];
-    } else if (type == GGML_TYPE_I16) {
-        v = (float) *(int16_t *) &data[i];
-    } else if (type == GGML_TYPE_I8) {
-        v = (float) *(int8_t *) &data[i];
-    } else if (type == GGML_TYPE_BF16) {
-        v = ggml_compute_bf16_to_fp32(*(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++) {
@@ -92,8 +50,25 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
                         LOG("..., ");
                         i0 = ne[0] - n;
                     }
-                    const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, 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(*(ggml_fp16_t *) &data[i]);
+                    } else if (type == GGML_TYPE_F32) {
+                        v = *(float *) &data[i];
+                    } else if (type == GGML_TYPE_I64) {
+                        v = (float) *(int64_t *) &data[i];
+                    } else if (type == GGML_TYPE_I32) {
+                        v = (float) *(int32_t *) &data[i];
+                    } else if (type == GGML_TYPE_I16) {
+                        v = (float) *(int16_t *) &data[i];
+                    } else if (type == GGML_TYPE_I8) {
+                        v = (float) *(int8_t *) &data[i];
+                    } else {
+                        GGML_ABORT("fatal error");
+                    }
                     LOG("%12.4f", v);
+                    sum += v;
                     if (i0 < ne[0] - 1) LOG(", ");
                 }
                 LOG("],\n");

examples/json_schema_to_grammar.py

@@ -586,10 +586,9 @@ class SchemaConverter:
             properties = list(schema.get('properties', {}).items())
             return self._add_rule(rule_name, self._build_object_rule(properties, required, name, schema.get('additionalProperties')))
 
-        elif schema_type in (None, 'object', 'string') and 'allOf' in schema:
+        elif schema_type in (None, 'object') and 'allOf' in schema:
             required = set()
             properties = []
-            enum_sets = []
             hybrid_name = name
             def add_component(comp_schema, is_required):
                 if (ref := comp_schema.get('$ref')) is not None:
@@ -601,9 +600,6 @@ class SchemaConverter:
                         if is_required:
                             required.add(prop_name)
 
-                if 'enum' in comp_schema:
-                    enum_sets.append(set(comp_schema['enum']))
-
             for t in schema['allOf']:
                 if 'anyOf' in t:
                     for tt in t['anyOf']:
@@ -611,15 +607,6 @@ class SchemaConverter:
                 else:
                     add_component(t, is_required=True)
 
-            if enum_sets:
-                enum_intersection = enum_sets[0]
-                for s in enum_sets[1:]:
-                    enum_intersection &= s
-
-                if enum_intersection:
-                    rule = '(' + ' | '.join((self._generate_constant_rule(v) for v in sorted(enum_intersection))) + ') space'
-                    return self._add_rule(rule_name, rule)
-
             return self._add_rule(rule_name, self._build_object_rule(properties, required, hybrid_name, additional_properties=None))
 
         elif schema_type in (None, 'array') and ('items' in schema or 'prefixItems' in schema):

examples/model-conversion/Makefile

@@ -63,7 +63,7 @@ causal-verify-logits: causal-run-original-model causal-run-converted-model
 	@MODEL_PATH="$(MODEL_PATH)" ./scripts/utils/check-nmse.py -m ${MODEL_PATH}
 
 causal-run-original-embeddings:
-	@./scripts/causal/run-casual-gen-embeddings-org.py
+	@./scripts/causal/run-casual-gen-embeddings-org.sh
 
 causal-run-converted-embeddings:
 	@./scripts/causal/run-converted-model-embeddings-logits.sh

examples/model-conversion/requirements.txt

@@ -1,6 +1,5 @@
 --extra-index-url https://download.pytorch.org/whl/cpu
-torch
-torchvision
-transformers
-huggingface-hub
-accelerate
+torch~=2.6.0
+torchvision~=0.21.0
+transformers~=4.55.0
+huggingface-hub~=0.34.0

examples/model-conversion/scripts/causal/compare-embeddings-logits.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#/bin/bash
 
 set -e
 

examples/model-conversion/scripts/causal/convert-model.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 

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

@@ -3,10 +3,11 @@
 import argparse
 import os
 import importlib
+import sys
 import torch
 import numpy as np
 
-from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoConfig, AutoModel, AutoModelForCausalLM
 from pathlib import Path
 
 unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
@@ -42,8 +43,6 @@ if unreleased_model_name:
         model = model_class.from_pretrained(model_path)
     except (ImportError, AttributeError) as e:
         print(f"Failed to import or load model: {e}")
-        print("Falling back to AutoModelForCausalLM")
-        model = AutoModelForCausalLM.from_pretrained(model_path)
 else:
     model = AutoModelForCausalLM.from_pretrained(model_path)
 print(f"Model class: {type(model)}")

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

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 

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

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 

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

@@ -9,134 +9,15 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
 import torch
 import numpy as np
 
-### If you want to dump RoPE activations, apply this monkey patch to the model
-### class from Transformers that you are running (replace apertus.modeling_apertus
-### with the proper package and class for your model
-### === START ROPE DEBUG ===
-# from transformers.models.apertus.modeling_apertus import apply_rotary_pos_emb
+unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
 
-# orig_rope = apply_rotary_pos_emb
-# torch.set_printoptions(threshold=float('inf'))
-# torch.set_printoptions(precision=6, sci_mode=False)
-
-# def debug_rope(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
-#     # log inputs
-#     summarize(q, "RoPE.q_in")
-#     summarize(k, "RoPE.k_in")
-
-#     # call original
-#     q_out, k_out = orig_rope(q, k, cos, sin, position_ids, unsqueeze_dim)
-
-#     # log outputs
-#     summarize(q_out, "RoPE.q_out")
-#     summarize(k_out, "RoPE.k_out")
-
-#     return q_out, k_out
-
-# # Patch it
-# import transformers.models.apertus.modeling_apertus as apertus_mod  # noqa: E402
-# apertus_mod.apply_rotary_pos_emb = debug_rope
-### == END ROPE DEBUG ===
-
-
-def summarize(tensor: torch.Tensor, name: str, max_seq: int = 3, max_vals: int = 3):
-    """
-    Print a tensor in llama.cpp debug style.
-
-    Supports:
-    - 2D tensors (seq, hidden)
-    - 3D tensors (batch, seq, hidden)
-    - 4D tensors (batch, seq, heads, dim_per_head) via flattening heads × dim_per_head
-
-    Shows first and last max_vals of each vector per sequence position.
-    """
-    t = tensor.detach().to(torch.float32).cpu()
-
-    # Determine dimensions
-    if t.ndim == 3:
-        _, s, _ = t.shape
-    elif t.ndim == 2:
-        _, s = 1, t.shape[0]
-        t = t.unsqueeze(0)
-    elif t.ndim == 4:
-        _, s, _, _ = t.shape
-    else:
-        print(f"Skipping tensor due to unsupported dimensions: {t.ndim}")
-        return
-
-    ten_shape = t.shape
-
-    print(f"ggml_debug: {name} = (f32)  ... = {{{ten_shape}}}")
-    print("                                     [")
-    print("                                      [")
-
-    # Determine indices for first and last sequences
-    first_indices = list(range(min(s, max_seq)))
-    last_indices = list(range(max(0, s - max_seq), s))
-
-    # Check if there's an overlap between first and last indices or if we're at the edge case of s = 2 * max_seq
-    has_overlap = bool(set(first_indices) & set(last_indices)) or (max_seq * 2 == s)
-
-    # Combine indices
-    if has_overlap:
-        # If there's overlap, just use the combined unique indices
-        indices = sorted(list(set(first_indices + last_indices)))
-        separator_index = None
-    else:
-        # If no overlap, we'll add a separator between first and last sequences
-        indices = first_indices + last_indices
-        separator_index = len(first_indices)
-
-    for i, si in enumerate(indices):
-        # Add separator if needed
-        if separator_index is not None and i == separator_index:
-            print("                                       ...")
-
-        # Extract appropriate slice
-        vec = t[0, si]
-        if vec.ndim == 2:  # 4D case: flatten heads × dim_per_head
-            flat = vec.flatten().tolist()
-        else:  # 2D or 3D case
-            flat = vec.tolist()
-
-        # First and last slices
-        first = flat[:max_vals]
-        last = flat[-max_vals:] if len(flat) >= max_vals else flat
-        first_str = ", ".join(f"{v:12.4f}" for v in first)
-        last_str = ", ".join(f"{v:12.4f}" for v in last)
-
-        print(f"                                       [{first_str}, ..., {last_str}]")
-
-    print("                                      ],")
-    print("                                     ]")
-    print(f"                                     sum = {t.sum().item():.6f}\n")
-
-
-def debug_hook(name):
-    def fn(_m, input, output):
-        if isinstance(input, torch.Tensor):
-            summarize(input, name + "_in")
-        elif isinstance(input, (tuple, list)) and isinstance(input[0], torch.Tensor):
-            summarize(input[0], name + "_in")
-        if isinstance(output, torch.Tensor):
-            summarize(output, name + "_out")
-        elif isinstance(output, (tuple, list)) and isinstance(output[0], torch.Tensor):
-            summarize(output[0], name + "_out")
-
-    return fn
-
-
-unreleased_model_name = os.getenv("UNRELEASED_MODEL_NAME")
-
-parser = argparse.ArgumentParser(description="Process model with specified path")
-parser.add_argument("--model-path", "-m", help="Path to the model")
+parser = argparse.ArgumentParser(description='Process model with specified path')
+parser.add_argument('--model-path', '-m', help='Path to the model')
 args = parser.parse_args()
 
-model_path = os.environ.get("MODEL_PATH", args.model_path)
+model_path = os.environ.get('MODEL_PATH', args.model_path)
 if model_path is None:
-    parser.error(
-        "Model path must be specified either via --model-path argument or MODEL_PATH environment variable"
-    )
+    parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable")
 
 config = AutoConfig.from_pretrained(model_path)
 
@@ -153,30 +34,18 @@ config = AutoConfig.from_pretrained(model_path)
 
 if unreleased_model_name:
     model_name_lower = unreleased_model_name.lower()
-    unreleased_module_path = (
-        f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
-    )
+    unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
     class_name = f"{unreleased_model_name}ForCausalLM"
     print(f"Importing unreleased model module: {unreleased_module_path}")
 
     try:
-        model_class = getattr(
-            importlib.import_module(unreleased_module_path), class_name
-        )
-        model = model_class.from_pretrained(
-            model_path
-        )  # Note: from_pretrained, not fromPretrained
+        model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
+        model = model_class.from_pretrained(model_path)  # Note: from_pretrained, not fromPretrained
     except (ImportError, AttributeError) as e:
         print(f"Failed to import or load model: {e}")
         exit(1)
 else:
-    model = AutoModelForCausalLM.from_pretrained(
-        model_path, device_map="auto", offload_folder="offload"
-    )
-
-for name, module in model.named_modules():
-    if len(list(module.children())) == 0:  # only leaf modules
-        module.register_forward_hook(debug_hook(name))
+    model = AutoModelForCausalLM.from_pretrained(model_path)
 
 model_name = os.path.basename(model_path)
 # Printing the Model class to allow for easier debugging. This can be useful

examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#/bin/bash
 
 set -e
 

examples/model-conversion/scripts/embedding/convert-model.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 

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

@@ -7,7 +7,7 @@ base_model:
 Recommended way to run this model:
 
 ```sh
-llama-server -hf {namespace}/{model_name}-GGUF --embeddings
+llama-server -hf {namespace}/{model_name}-GGUF
 ```
 
 Then the endpoint can be accessed at http://localhost:8080/embedding, for

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

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 

examples/model-conversion/scripts/utils/create-collection-add-model.sh

@@ -1,6 +1,4 @@
 
-#!/usr/bin/env bash
-
 COLLECTION_SLUG=$(python ./create_collection.py --return-slug)
 echo "Created collection: $COLLECTION_SLUG"
 

examples/model-conversion/scripts/utils/curl-embedding-server.sh

@@ -1,6 +0,0 @@
-#!/usr/bin/env bash
-curl --request POST \
-    --url http://localhost:8080/embedding \
-    --header "Content-Type: application/json" \
-    --data '{"input": "Hello world today"}' \
-    --silent

examples/model-conversion/scripts/utils/inspect-converted-model.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 # First try command line argument, then environment variable, then file
 CONVERTED_MODEL="${1:-"$CONVERTED_MODEL"}"

examples/model-conversion/scripts/utils/inspect-org-model.py

@@ -40,7 +40,7 @@ if os.path.exists(index_path):
         file_path = os.path.join(model_path, file_name)
         print(f"\n--- From {file_name} ---")
 
-        with safe_open(file_path, framework="pt") as f:  # type: ignore
+        with safe_open(file_path, framework="pt") as f:
             for tensor_name in sorted(tensor_names):
                 tensor = f.get_tensor(tensor_name)
                 print(f"- {tensor_name} : shape = {tensor.shape}, dtype = {tensor.dtype}")
@@ -49,7 +49,7 @@ elif os.path.exists(single_file_path):
     # Single file model (original behavior)
     print("Single-file model detected")
 
-    with safe_open(single_file_path, framework="pt") as f:  # type: ignore
+    with safe_open(single_file_path, framework="pt") as f:
         keys = f.keys()
         print("Tensors in model:")
         for key in sorted(keys):

examples/model-conversion/scripts/utils/perplexity-gen.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 

examples/model-conversion/scripts/utils/perplexity-run-simple.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 

examples/model-conversion/scripts/utils/perplexity-run.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 

examples/model-conversion/scripts/utils/quantize.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 

examples/model-conversion/scripts/utils/run-embedding-server.sh

@@ -1,4 +1,4 @@
-#!/usr/bin/env bash
+#!/bin/bash
 
 set -e
 #

examples/simple/simple.cpp

@@ -145,20 +145,6 @@ int main(int argc, char ** argv) {
 
     llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size());
 
-    if (llama_model_has_encoder(model)) {
-        if (llama_encode(ctx, batch)) {
-            fprintf(stderr, "%s : failed to eval\n", __func__);
-            return 1;
-        }
-
-        llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
-        if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
-            decoder_start_token_id = llama_vocab_bos(vocab);
-        }
-
-        batch = llama_batch_get_one(&decoder_start_token_id, 1);
-    }
-
     // main loop
 
     const auto t_main_start = ggml_time_us();

examples/speculative/speculative.cpp

@@ -244,7 +244,7 @@ int main(int argc, char ** argv) {
                     // stochastic verification
                     common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
 
-                    auto & dist_tgt = *common_sampler_get_candidates(smpl, true);
+                    auto & dist_tgt = *common_sampler_get_candidates(smpl);
 
                     float p_tgt = 0.0f;
                     float p_dft = 0.0f;
@@ -493,7 +493,7 @@ int main(int argc, char ** argv) {
 
                 common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
 
-                const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl, true);
+                const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl);
 
                 for (int k = 0; k < std::min(n_seq_dft + 3, (int) cur_p->size); ++k) {
                     LOG_DBG(" - draft candidate %3d for seq %3d, pos %3d: %6d (%8.3f) '%s'\n",

ggml/CMakeLists.txt

@@ -1,41 +1,5 @@
 cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories.
-project("ggml" C CXX ASM)
-
-### GGML Version
-set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 9)
-set(GGML_VERSION_PATCH 0)
-set(GGML_VERSION_DEV "-dev")  # "-dev" for development, "" for releases
-set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
-
-find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)
-if(GIT_EXE)
-    # Get current git commit hash
-    execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
-        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
-        OUTPUT_VARIABLE GGML_BUILD_COMMIT
-        OUTPUT_STRIP_TRAILING_WHITESPACE
-        ERROR_QUIET
-    )
-
-    # Check if the working directory is dirty (i.e., has uncommitted changes)
-    execute_process(COMMAND ${GIT_EXE} diff-index --quiet HEAD -- .
-        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
-        RESULT_VARIABLE GGML_GIT_DIRTY
-        ERROR_QUIET
-    )
-endif()
-
-# Build the version string with optional -dev suffix and dirty flag
-set(GGML_VERSION "${GGML_VERSION_BASE}${GGML_VERSION_DEV}")
-if(GGML_GIT_DIRTY AND NOT GGML_GIT_DIRTY EQUAL 0)
-    set(GGML_VERSION "${GGML_VERSION}-dirty")
-endif()
-
-if(NOT GGML_BUILD_COMMIT)
-    set(GGML_BUILD_COMMIT "unknown")
-endif()
-
+project("ggml" C CXX)
 include(CheckIncludeFileCXX)
 
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
@@ -165,11 +129,10 @@ endif()
 option(GGML_LASX             "ggml: enable lasx"             ON)
 option(GGML_LSX              "ggml: enable lsx"              ON)
 option(GGML_RVV              "ggml: enable rvv"              ON)
-option(GGML_RV_ZFH           "ggml: enable riscv zfh"        ON)
-option(GGML_RV_ZVFH          "ggml: enable riscv zvfh"       ON)
-option(GGML_RV_ZICBOP        "ggml: enable riscv zicbop"     ON)
+option(GGML_RV_ZFH           "ggml: enable riscv zfh"        OFF)
 option(GGML_XTHEADVECTOR     "ggml: enable xtheadvector"     OFF)
 option(GGML_VXE              "ggml: enable vxe"              ON)
+option(GGML_NNPA             "ggml: enable nnpa"             OFF)  # temp disabled by default, see: https://github.com/ggml-org/llama.cpp/issues/14877
 
 option(GGML_CPU_ALL_VARIANTS "ggml: build all variants of the CPU backend (requires GGML_BACKEND_DL)" OFF)
 set(GGML_CPU_ARM_ARCH        "" CACHE STRING "ggml: CPU architecture for ARM")
@@ -226,6 +189,7 @@ option(GGML_WEBGPU                          "ggml: use WebGPU"
 option(GGML_WEBGPU_DEBUG                    "ggml: enable WebGPU debug output"                OFF)
 option(GGML_ZDNN                            "ggml: use zDNN"                                  OFF)
 option(GGML_METAL                           "ggml: use Metal"                                 ${GGML_METAL_DEFAULT})
+option(GGML_METAL_USE_BF16                  "ggml: use bfloat if available"                   OFF)
 option(GGML_METAL_NDEBUG                    "ggml: disable Metal debugging"                   OFF)
 option(GGML_METAL_SHADER_DEBUG              "ggml: compile Metal with -fno-fast-math"         OFF)
 option(GGML_METAL_EMBED_LIBRARY             "ggml: embed Metal library"                       ${GGML_METAL})
@@ -336,6 +300,26 @@ endif()
 # Create CMake package
 #
 
+# Generate version info based on git commit.
+
+if(NOT DEFINED GGML_BUILD_NUMBER)
+    find_program(GIT_EXE NAMES git git.exe REQUIRED NO_CMAKE_FIND_ROOT_PATH)
+    execute_process(COMMAND ${GIT_EXE} rev-list --count HEAD
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+        OUTPUT_VARIABLE GGML_BUILD_NUMBER
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+    )
+
+    if(GGML_BUILD_NUMBER EQUAL 1)
+        message(WARNING "GGML build version fixed at 1 likely due to a shallow clone.")
+    endif()
+
+    execute_process(COMMAND ${GIT_EXE} rev-parse --short HEAD
+        WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
+        OUTPUT_VARIABLE GGML_BUILD_COMMIT
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+    )
+endif()
 
 
 # Capture variables prefixed with GGML_.
@@ -364,7 +348,7 @@ set(GGML_VARIABLES_EXPANDED ${variable_set_statements})
 
 # Create the CMake package and set install location.
 
-set(GGML_INSTALL_VERSION ${GGML_VERSION})
+set(GGML_INSTALL_VERSION 0.0.${GGML_BUILD_NUMBER})
 set(GGML_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header  files")
 set(GGML_LIB_INSTALL_DIR     ${CMAKE_INSTALL_LIBDIR}     CACHE PATH "Location of library files")
 set(GGML_BIN_INSTALL_DIR     ${CMAKE_INSTALL_BINDIR}     CACHE PATH "Location of binary  files")

ggml/include/ggml-backend.h

@@ -132,8 +132,6 @@ extern "C" {
         GGML_BACKEND_DEVICE_TYPE_CPU,
         // GPU device using dedicated memory
         GGML_BACKEND_DEVICE_TYPE_GPU,
-        // integrated GPU device using host memory
-        GGML_BACKEND_DEVICE_TYPE_IGPU,
         // accelerator devices intended to be used together with the CPU backend (e.g. BLAS or AMX)
         GGML_BACKEND_DEVICE_TYPE_ACCEL
     };
@@ -152,21 +150,11 @@ extern "C" {
 
     // all the device properties
     struct ggml_backend_dev_props {
-        // device name
         const char * name;
-        // device description
         const char * description;
-        // device free memory in bytes
         size_t memory_free;
-        // device total memory in bytes
         size_t memory_total;
-        // device type
         enum ggml_backend_dev_type type;
-        // device id
-        //   for PCI devices, this should be the PCI bus id formatted as "domain:bus:device.function" (e.g. "0000:01:00.0")
-        //   if the id is unknown, this should be NULL
-        const char * device_id;
-        // device capabilities
         struct ggml_backend_dev_caps caps;
     };
 
@@ -319,9 +307,6 @@ extern "C" {
     GGML_API void                 ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
     GGML_API ggml_backend_t       ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
 
-    // Split graph without allocating it
-    GGML_API void                 ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
-
     // Allocate and compute graph on the backend scheduler
     GGML_API bool                 ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // returns success
     GGML_API enum ggml_status     ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);

ggml/include/ggml-cpu.h

@@ -101,6 +101,7 @@ extern "C" {
     GGML_BACKEND_API int ggml_cpu_has_riscv_v    (void);
     GGML_BACKEND_API int ggml_cpu_has_vsx        (void);
     GGML_BACKEND_API int ggml_cpu_has_vxe        (void);
+    GGML_BACKEND_API int ggml_cpu_has_nnpa       (void);
     GGML_BACKEND_API int ggml_cpu_has_wasm_simd  (void);
     GGML_BACKEND_API int ggml_cpu_has_llamafile  (void);
 
@@ -134,7 +135,6 @@ extern "C" {
     GGML_BACKEND_API ggml_backend_reg_t ggml_backend_cpu_reg(void);
 
     GGML_BACKEND_API void ggml_cpu_fp32_to_fp32(const float *,       float *, int64_t);
-    GGML_BACKEND_API void ggml_cpu_fp32_to_i32 (const float *,     int32_t *, int64_t);
     GGML_BACKEND_API void ggml_cpu_fp32_to_fp16(const float *, ggml_fp16_t *, int64_t);
     GGML_BACKEND_API void ggml_cpu_fp16_to_fp32(const ggml_fp16_t *, float *, int64_t);
     GGML_BACKEND_API void ggml_cpu_fp32_to_bf16(const float *, ggml_bf16_t *, int64_t);

ggml/include/ggml-metal.h

@@ -39,13 +39,18 @@ extern "C" {
 // user-code should use only these functions
 //
 
-// TODO: remove in the future
 GGML_BACKEND_API ggml_backend_t ggml_backend_metal_init(void);
 
 GGML_BACKEND_API bool ggml_backend_is_metal(ggml_backend_t backend);
 
+GGML_DEPRECATED(
+        GGML_BACKEND_API ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, size_t size, size_t max_size),
+        "obsoleted by the new device interface - https://github.com/ggml-org/llama.cpp/pull/9713");
+
 GGML_BACKEND_API void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data);
 
+GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
+
 // helper to check if the device supports a specific family
 // ideally, the user code should be doing these checks
 // ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf

ggml/include/ggml-zdnn.h

@@ -7,6 +7,8 @@
 extern "C" {
 #endif
 
+GGML_BACKEND_API ggml_backend_t ggml_backend_zdnn_init(void);
+
 GGML_BACKEND_API ggml_backend_reg_t ggml_backend_zdnn_reg(void);
 
 #ifdef __cplusplus

ggml/include/ggml.h

@@ -284,19 +284,19 @@ __host__ __device__ constexpr inline void ggml_unused_vars_impl(Args&&...) noexc
 //    GGML_TENSOR_LOCALS(size_t,  nb1, src1, nb);
 //
 #define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \
-    const type prefix##0 = (pointer) ? (pointer)->array[0] : 0; \
+    const type prefix##0 = (pointer)->array[0]; \
     GGML_UNUSED(prefix##0);
 #define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \
     GGML_TENSOR_LOCALS_1    (type, prefix, pointer, array) \
-    const type prefix##1 = (pointer) ? (pointer)->array[1] : 0; \
+    const type prefix##1 = (pointer)->array[1]; \
     GGML_UNUSED(prefix##1);
 #define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \
     GGML_TENSOR_LOCALS_2    (type, prefix, pointer, array) \
-    const type prefix##2 = (pointer) ? (pointer)->array[2] : 0; \
+    const type prefix##2 = (pointer)->array[2]; \
     GGML_UNUSED(prefix##2);
 #define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \
     GGML_TENSOR_LOCALS_3  (type, prefix, pointer, array) \
-    const type prefix##3 = (pointer) ? (pointer)->array[3] : 0; \
+    const type prefix##3 = (pointer)->array[3]; \
     GGML_UNUSED(prefix##3);
 
 #define GGML_TENSOR_UNARY_OP_LOCALS \
@@ -511,7 +511,6 @@ extern "C" {
         GGML_OP_CONV_TRANSPOSE_1D,
         GGML_OP_IM2COL,
         GGML_OP_IM2COL_BACK,
-        GGML_OP_IM2COL_3D,
         GGML_OP_CONV_2D,
         GGML_OP_CONV_3D,
         GGML_OP_CONV_2D_DW,
@@ -1404,7 +1403,6 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
-    // note: casting from f32 to i32 will discard the fractional part
     GGML_API struct ggml_tensor * ggml_cast(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1529,11 +1527,7 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
 
-    // supports 4D a:
-    // a     [n_embd, ne1, ne2, ne3]
-    // b I32 [n_rows, ne2, ne3, 1]
-    //
-    // return [n_embd, n_rows, ne2, ne3]
+    // supports 3D: a->ne[2] == b->ne[1]
     GGML_API struct ggml_tensor * ggml_get_rows(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,  // data
@@ -1876,41 +1870,6 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
-    GGML_API struct ggml_tensor * ggml_im2col_3d(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            struct ggml_tensor  * b,
-            int64_t               IC,
-            int                   s0, // stride width
-            int                   s1, // stride height
-            int                   s2, // stride depth
-            int                   p0, // padding width
-            int                   p1, // padding height
-            int                   p2, // padding depth
-            int                   d0, // dilation width
-            int                   d1, // dilation height
-            int                   d2, // dilation depth
-            enum ggml_type        dst_type);
-
-    // a: [OC*IC, KD, KH, KW]
-    // b: [N*IC, ID, IH, IW]
-    // result: [N*OC, OD, OH, OW]
-    GGML_API struct ggml_tensor * ggml_conv_3d(
-                struct ggml_context * ctx,
-                struct ggml_tensor  * a,
-                struct ggml_tensor  * b,
-                int64_t               IC,
-                int                   s0, // stride width
-                int                   s1, // stride height
-                int                   s2, // stride depth
-                int                   p0, // padding width
-                int                   p1, // padding height
-                int                   p2, // padding depth
-                int                   d0, // dilation width
-                int                   d1, // dilation height
-                int                   d2  // dilation depth
-        );
-
     // kernel size is a->ne[0] x a->ne[1]
     // stride is equal to kernel size
     // padding is zero
@@ -1982,7 +1941,7 @@ extern "C" {
             int                   d0,  // dilation dimension 0
             int                   d1); // dilation dimension 1
 
-    GGML_API struct ggml_tensor * ggml_conv_3d_direct(
+    GGML_API struct ggml_tensor * ggml_conv_3d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,   // kernel [KW, KH, KD, IC * OC]
             struct ggml_tensor  * b,   // input  [W, H, D, C * N]
@@ -2089,19 +2048,6 @@ extern "C" {
             int                  p2,
             int                  p3);
 
-    GGML_API struct ggml_tensor * ggml_pad_ext(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * a,
-            int                  lp0,
-            int                  rp0,
-            int                  lp1,
-            int                  rp1,
-            int                  lp2,
-            int                  rp2,
-            int                  lp3,
-            int                  rp3
-            );
-
     // pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c]
     GGML_API struct ggml_tensor * ggml_pad_reflect_1d(
             struct ggml_context * ctx,

ggml/src/CMakeLists.txt

@@ -114,9 +114,6 @@ message(STATUS "GGML_SYSTEM_ARCH: ${GGML_SYSTEM_ARCH}")
 
 if (NOT MSVC)
     if (GGML_STATIC)
-        if (UNIX AND NOT APPLE)
-            set(CMAKE_FIND_LIBRARY_SUFFIXES ".a;.so")
-        endif()
         add_link_options(-static)
         if (MINGW)
             add_link_options(-static-libgcc -static-libstdc++)

ggml/src/ggml-backend-impl.h

@@ -8,7 +8,7 @@
 extern "C" {
 #endif
 
-    #define GGML_BACKEND_API_VERSION 2
+    #define GGML_BACKEND_API_VERSION 1
 
     //
     // Backend buffer type
@@ -114,9 +114,6 @@ extern "C" {
         void (*event_record)(ggml_backend_t backend, ggml_backend_event_t event);
         // wait for an event on on a different stream
         void (*event_wait)  (ggml_backend_t backend, ggml_backend_event_t event);
-
-        // (optional) sort/optimize the nodes in the graph
-        void                      (*graph_optimize)    (ggml_backend_t backend, struct ggml_cgraph * cgraph);
     };
 
     struct ggml_backend {

ggml/src/ggml-backend-reg.cpp

@@ -400,8 +400,9 @@ ggml_backend_t ggml_backend_init_by_type(enum ggml_backend_dev_type type, const
 
 ggml_backend_t ggml_backend_init_best(void) {
     ggml_backend_dev_t dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_GPU);
-    dev = dev ? dev : ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_IGPU);
-    dev = dev ? dev : ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+    if (!dev) {
+        dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+    }
     if (!dev) {
         return nullptr;
     }

ggml/src/ggml-backend.cpp

@@ -31,7 +31,6 @@
 // backend buffer type
 
 const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
-    GGML_ASSERT(buft);
     return buft->iface.get_name(buft);
 }
 
@@ -41,17 +40,14 @@ ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t
         return ggml_backend_buffer_init(buft, {}, NULL, 0);
     }
 
-    GGML_ASSERT(buft);
     return buft->iface.alloc_buffer(buft, size);
 }
 
 size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) {
-    GGML_ASSERT(buft);
     return buft->iface.get_alignment(buft);
 }
 
 size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) {
-    GGML_ASSERT(buft);
     // get_max_size is optional, defaults to SIZE_MAX
     if (buft->iface.get_max_size) {
         return buft->iface.get_max_size(buft);
@@ -60,7 +56,6 @@ size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) {
 }
 
 size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor) {
-    GGML_ASSERT(buft);
     // get_alloc_size is optional, defaults to ggml_nbytes
     if (buft->iface.get_alloc_size) {
         size_t size = buft->iface.get_alloc_size(buft, tensor);
@@ -71,7 +66,6 @@ size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, const s
 }
 
 bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
-    GGML_ASSERT(buft);
     if (buft->iface.is_host) {
         return buft->iface.is_host(buft);
     }
@@ -79,7 +73,6 @@ bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) {
 }
 
 ggml_backend_dev_t ggml_backend_buft_get_device(ggml_backend_buffer_type_t buft) {
-    GGML_ASSERT(buft);
     return buft->device;
 }
 
@@ -117,12 +110,10 @@ void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
 }
 
 size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
-    GGML_ASSERT(buffer);
     return buffer->size;
 }
 
 void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
-    GGML_ASSERT(buffer);
     // get_base is optional if the buffer is zero-sized
     if (buffer->size == 0) {
         return NULL;
@@ -136,7 +127,6 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
 }
 
 enum ggml_status ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
-    GGML_ASSERT(buffer);
     // init_tensor is optional
     if (buffer->iface.init_tensor) {
         return buffer->iface.init_tensor(buffer, tensor);
@@ -145,7 +135,6 @@ enum ggml_status ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, s
 }
 
 void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    GGML_ASSERT(buffer);
     // clear is optional if the buffer is zero-sized
     if (buffer->size == 0) {
         return;
@@ -171,7 +160,6 @@ bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) {
 }
 
 void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
-    GGML_ASSERT(buffer);
     buffer->usage = usage;
 
     // FIXME: add a generic callback to the buffer interface
@@ -181,17 +169,14 @@ void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backe
 }
 
 enum ggml_backend_buffer_usage ggml_backend_buffer_get_usage(ggml_backend_buffer_t buffer) {
-    GGML_ASSERT(buffer);
     return buffer->usage;
 }
 
 ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) {
-    GGML_ASSERT(buffer);
     return buffer->buft;
 }
 
 void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) {
-    GGML_ASSERT(buffer);
     if (buffer->iface.reset) {
         buffer->iface.reset(buffer);
     }
@@ -230,7 +215,6 @@ void ggml_backend_free(ggml_backend_t backend) {
 }
 
 ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) {
-    GGML_ASSERT(backend);
     return ggml_backend_dev_buffer_type(backend->device);
 }
 
@@ -247,8 +231,6 @@ size_t ggml_backend_get_max_size(ggml_backend_t backend) {
 }
 
 void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    GGML_ASSERT(backend);
-    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
@@ -260,8 +242,6 @@ void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor *
 }
 
 void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    GGML_ASSERT(backend);
-    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
@@ -303,7 +283,6 @@ void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, siz
 }
 
 void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
-    GGML_ASSERT(tensor);
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
     if (size == 0) {
@@ -319,7 +298,6 @@ void ggml_backend_tensor_memset(struct ggml_tensor * tensor, uint8_t value, size
 }
 
 void ggml_backend_synchronize(ggml_backend_t backend) {
-    GGML_ASSERT(backend);
     if (backend->iface.synchronize == NULL) {
         return;
     }
@@ -328,21 +306,18 @@ void ggml_backend_synchronize(ggml_backend_t backend) {
 }
 
 ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.graph_plan_create != NULL);
 
     return backend->iface.graph_plan_create(backend, cgraph);
 }
 
 void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.graph_plan_free != NULL);
 
     backend->iface.graph_plan_free(backend, plan);
 }
 
 enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.graph_plan_compute != NULL);
 
     return backend->iface.graph_plan_compute(backend, plan);
@@ -355,27 +330,22 @@ enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_
 }
 
 enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    GGML_ASSERT(backend);
     return backend->iface.graph_compute(backend, cgraph);
 }
 
 bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
-    GGML_ASSERT(backend);
     return ggml_backend_dev_supports_op(backend->device, op);
 }
 
 bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) {
-    GGML_ASSERT(backend);
     return ggml_backend_dev_supports_buft(backend->device, buft);
 }
 
 bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) {
-    GGML_ASSERT(backend);
     return ggml_backend_dev_offload_op(backend->device, op);
 }
 
 ggml_backend_dev_t ggml_backend_get_device(ggml_backend_t backend) {
-    GGML_ASSERT(backend);
     return backend->device;
 }
 
@@ -411,7 +381,6 @@ void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t b
         return;
     }
 
-    GGML_ASSERT(backend_dst);
     if (backend_dst->iface.cpy_tensor_async != NULL) {
         if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
             return;
@@ -443,52 +412,38 @@ void ggml_backend_event_free(ggml_backend_event_t event) {
 }
 
 void ggml_backend_event_record(ggml_backend_event_t event, ggml_backend_t backend) {
-    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.event_record != NULL);
 
     backend->iface.event_record(backend, event);
 }
 
 void ggml_backend_event_synchronize(ggml_backend_event_t event) {
-    GGML_ASSERT(event);
     GGML_ASSERT(event->device->iface.event_synchronize);
 
     event->device->iface.event_synchronize(event->device, event);
 }
 
 void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
-    GGML_ASSERT(backend);
     GGML_ASSERT(backend->iface.event_wait != NULL);
 
     backend->iface.event_wait(backend, event);
 }
 
-static void ggml_backend_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    GGML_ASSERT(backend);
-    if (backend->iface.graph_optimize != NULL) {
-        backend->iface.graph_optimize(backend, cgraph);
-    }
-}
-
 // Backend device
 
 const char * ggml_backend_dev_name(ggml_backend_dev_t device) {
-    GGML_ASSERT(device);
     return device->iface.get_name(device);
 }
 
 const char * ggml_backend_dev_description(ggml_backend_dev_t device) {
-    GGML_ASSERT(device);
     return device->iface.get_description(device);
 }
 
 void ggml_backend_dev_memory(ggml_backend_dev_t device, size_t * free, size_t * total) {
-    GGML_ASSERT(device);
     device->iface.get_memory(device, free, total);
 }
 
 enum ggml_backend_dev_type ggml_backend_dev_type(ggml_backend_dev_t device) {
-    GGML_ASSERT(device);
     return device->iface.get_type(device);
 }
 
@@ -498,22 +453,18 @@ void ggml_backend_dev_get_props(ggml_backend_dev_t device, struct ggml_backend_d
 }
 
 ggml_backend_reg_t ggml_backend_dev_backend_reg(ggml_backend_dev_t device) {
-    GGML_ASSERT(device);
     return device->reg;
 }
 
 ggml_backend_t ggml_backend_dev_init(ggml_backend_dev_t device, const char * params) {
-    GGML_ASSERT(device);
     return device->iface.init_backend(device, params);
 }
 
 ggml_backend_buffer_type_t ggml_backend_dev_buffer_type(ggml_backend_dev_t device) {
-    GGML_ASSERT(device);
     return device->iface.get_buffer_type(device);
 }
 
 ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t device) {
-    GGML_ASSERT(device);
     if (device->iface.get_host_buffer_type == NULL) {
         return NULL;
     }
@@ -522,22 +473,18 @@ ggml_backend_buffer_type_t ggml_backend_dev_host_buffer_type(ggml_backend_dev_t
 }
 
 ggml_backend_buffer_t ggml_backend_dev_buffer_from_host_ptr(ggml_backend_dev_t device, void * ptr, size_t size, size_t max_tensor_size) {
-    GGML_ASSERT(device);
     return device->iface.buffer_from_host_ptr(device, ptr, size, max_tensor_size);
 }
 
 bool ggml_backend_dev_supports_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
-    GGML_ASSERT(device);
     return device->iface.supports_op(device, op);
 }
 
 bool ggml_backend_dev_supports_buft(ggml_backend_dev_t device, ggml_backend_buffer_type_t buft) {
-    GGML_ASSERT(device);
     return device->iface.supports_buft(device, buft);
 }
 
 bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_tensor * op) {
-    GGML_ASSERT(device);
     if (device->iface.offload_op != NULL) {
         return device->iface.offload_op(device, op);
     }
@@ -548,22 +495,18 @@ bool ggml_backend_dev_offload_op(ggml_backend_dev_t device, const struct ggml_te
 // Backend (reg)
 
 const char * ggml_backend_reg_name(ggml_backend_reg_t reg) {
-    GGML_ASSERT(reg);
     return reg->iface.get_name(reg);
 }
 
 size_t ggml_backend_reg_dev_count(ggml_backend_reg_t reg) {
-    GGML_ASSERT(reg);
     return reg->iface.get_device_count(reg);
 }
 
 ggml_backend_dev_t ggml_backend_reg_dev_get(ggml_backend_reg_t reg, size_t index) {
-    GGML_ASSERT(reg);
     return reg->iface.get_device(reg, index);
 }
 
 void * ggml_backend_reg_get_proc_address(ggml_backend_reg_t reg, const char * name) {
-    GGML_ASSERT(reg);
     if (!reg->iface.get_proc_address) {
         return NULL;
     }
@@ -578,7 +521,6 @@ struct ggml_backend_multi_buffer_context {
 };
 
 static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    GGML_ASSERT(buffer);
     ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
     for (size_t i = 0; i < ctx->n_buffers; i++) {
         ggml_backend_buffer_free(ctx->buffers[i]);
@@ -589,7 +531,6 @@ static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer)
 }
 
 static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    GGML_ASSERT(buffer);
     ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
     for (size_t i = 0; i < ctx->n_buffers; i++) {
         ggml_backend_buffer_clear(ctx->buffers[i], value);
@@ -625,12 +566,10 @@ ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer
 }
 
 bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) {
-    GGML_ASSERT(buffer);
     return buffer->iface.free_buffer == ggml_backend_multi_buffer_free_buffer;
 }
 
 void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) {
-    GGML_ASSERT(buffer);
     GGML_ASSERT(ggml_backend_buffer_is_multi_buffer(buffer));
     ggml_backend_multi_buffer_context * ctx = (ggml_backend_multi_buffer_context *) buffer->context;
     for (size_t i = 0; i < ctx->n_buffers; i++) {
@@ -658,7 +597,7 @@ static bool ggml_is_view_op(enum ggml_op op) {
 #endif
 
 #ifndef GGML_SCHED_MAX_SPLIT_INPUTS
-#define GGML_SCHED_MAX_SPLIT_INPUTS 30
+#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC
 #endif
 
 #ifndef GGML_SCHED_MAX_COPIES
@@ -909,7 +848,7 @@ static void ggml_backend_sched_set_if_supported(ggml_backend_sched_t sched, stru
 }
 
 // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
-void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
     // reset splits
     sched->n_splits = 0;
     sched->n_graph_inputs = 0;
@@ -1305,10 +1244,6 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra
         struct ggml_backend_sched_split * split = &sched->splits[i];
         split->graph = ggml_graph_view(graph, split->i_start, split->i_end);
 
-        // Optimize this split of the graph. This needs to happen before we make graph_copy,
-        // so they are in sync.
-        ggml_backend_graph_optimize(sched->backends[split->backend_id], &split->graph);
-
         // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split
         for (int j = 0; j < split->n_inputs; j++) {
             assert(graph_copy->size > (graph_copy->n_nodes + 1));
@@ -1414,7 +1349,6 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
 }
 
 static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
-    GGML_ASSERT(sched);
     struct ggml_backend_sched_split * splits = sched->splits;
 
     ggml_tensor * prev_ids_tensor = nullptr;
@@ -1683,7 +1617,6 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
 }
 
 void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
-    GGML_ASSERT(sched);
     // reset state for the next run
     if (!sched->is_reset) {
         ggml_hash_set_reset(&sched->hash_set);
@@ -1695,11 +1628,8 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
 }
 
 bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
-    GGML_ASSERT(sched);
     GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
 
-    ggml_backend_sched_reset(sched);
-
     ggml_backend_sched_synchronize(sched);
 
     ggml_backend_sched_split_graph(sched, measure_graph);
@@ -1714,7 +1644,6 @@ bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph *
 }
 
 bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    GGML_ASSERT(sched);
     GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + graph->n_leafs);
     GGML_ASSERT(!sched->is_alloc);
 
@@ -1739,7 +1668,6 @@ enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, st
 }
 
 enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    GGML_ASSERT(sched);
     if (!sched->is_reset && !sched->is_alloc) {
         ggml_backend_sched_reset(sched);
     }
@@ -1754,7 +1682,6 @@ enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sch
 }
 
 void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
-    GGML_ASSERT(sched);
     for (int i = 0; i < sched->n_backends; i++) {
         ggml_backend_synchronize(sched->backends[i]);
     }
@@ -1767,34 +1694,28 @@ void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
 }
 
 void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
-    GGML_ASSERT(sched);
     sched->callback_eval = callback;
     sched->callback_eval_user_data = user_data;
 }
 
 int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
-    GGML_ASSERT(sched);
     return sched->n_splits;
 }
 
 int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
-    GGML_ASSERT(sched);
     return sched->n_copies;
 }
 
 int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched) {
-    GGML_ASSERT(sched);
     return sched->n_backends;
 }
 
 ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i) {
-    GGML_ASSERT(sched);
     GGML_ASSERT(i >= 0 && i < sched->n_backends);
     return sched->backends[i];
 }
 
 size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
-    GGML_ASSERT(sched);
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
 
@@ -1802,7 +1723,6 @@ size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backe
 }
 
 void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
-    GGML_ASSERT(sched);
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
     tensor_backend_id(node) = backend_index;
@@ -1811,7 +1731,6 @@ void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct gg
 }
 
 ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
-    GGML_ASSERT(sched);
     int backend_index = tensor_backend_id(node);
     if (backend_index == -1) {
         return NULL;
@@ -1822,7 +1741,6 @@ ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched,
 // utils
 
 enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor) {
-    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->buffer == NULL);
     GGML_ASSERT(tensor->view_src != NULL);
     GGML_ASSERT(tensor->view_src->buffer != NULL);
@@ -1834,7 +1752,6 @@ enum ggml_status ggml_backend_view_init(struct ggml_tensor * tensor) {
 }
 
 enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) {
-    GGML_ASSERT(tensor);
     GGML_ASSERT(tensor->buffer == NULL);
     GGML_ASSERT(tensor->data == NULL);
     GGML_ASSERT(tensor->view_src == NULL);
@@ -1908,7 +1825,6 @@ static void graph_copy_init_tensor(struct ggml_hash_set * hash_set, struct ggml_
 }
 
 struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) {
-    GGML_ASSERT(graph);
     struct ggml_hash_set hash_set = ggml_hash_set_new(graph->visited_hash_set.size);
     struct ggml_tensor ** node_copies = (ggml_tensor **) calloc(hash_set.size, sizeof(node_copies[0])); // NOLINT
     bool * node_init = (bool *) calloc(hash_set.size, sizeof(node_init[0]));
@@ -2053,7 +1969,6 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t
 // CPU backend - buffer
 
 static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
-    GGML_ASSERT(buffer);
     uintptr_t data = (uintptr_t)buffer->context;
 
     // align the buffer
@@ -2065,33 +1980,28 @@ static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) {
 }
 
 static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
-    GGML_ASSERT(buffer);
     ggml_aligned_free(buffer->context, buffer->size);
 }
 
 static void ggml_backend_cpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
-    GGML_ASSERT(tensor);
     memset((char *)tensor->data + offset, value, size);
 
     GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
-    GGML_ASSERT(tensor);
     memcpy((char *)tensor->data + offset, data, size);
 
     GGML_UNUSED(buffer);
 }
 
 static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
-    GGML_ASSERT(tensor);
     memcpy(data, (const char *)tensor->data + offset, size);
 
     GGML_UNUSED(buffer);
 }
 
 static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) {
-    GGML_ASSERT(src);
     if (ggml_backend_buffer_is_host(src->buffer)) {
         memcpy(dst->data, src->data, ggml_nbytes(src));
         return true;
@@ -2102,7 +2012,6 @@ static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, con
 }
 
 static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) {
-    GGML_ASSERT(buffer);
     memset(buffer->context, value, buffer->size);
 }
 

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

@@ -270,7 +270,6 @@ static struct ggml_backend_i blas_backend_i = {
     /* .graph_compute           = */ ggml_backend_blas_graph_compute,
     /* .event_record            = */ NULL,
     /* .event_wait              = */ NULL,
-    /* .graph_optimize          = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_blas_guid(void) {

ggml/src/ggml-cann/aclnn_ops.cpp

@@ -70,8 +70,6 @@
 #include <aclnnop/aclnn_zero.h>
 #include <aclnnop/aclnn_index_copy.h>
 #include <aclnnop/aclnn_index_select.h>
-#include <aclnnop/aclnn_clamp.h>
-#include <aclnnop/aclnn_threshold.h>
 #include <float.h>
 
 #include <cmath>
@@ -589,16 +587,9 @@ void ggml_cann_pad(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     // the position of elements in the array means which dirction to padding,
     // each position means: [dim0.front, dim0.behind, dim1.front, dim1.behind,
     //                       dim2.front, dim2.behind, dim3.front, dim3.behind]
-    const int32_t lp0 = ggml_get_op_params_i32(dst, 0);
-    const int32_t rp0 = ggml_get_op_params_i32(dst, 1);
-    const int32_t lp1 = ggml_get_op_params_i32(dst, 2);
-    const int32_t rp1 = ggml_get_op_params_i32(dst, 3);
-    const int32_t lp2 = ggml_get_op_params_i32(dst, 4);
-    const int32_t rp2 = ggml_get_op_params_i32(dst, 5);
-    const int32_t lp3 = ggml_get_op_params_i32(dst, 6);
-    const int32_t rp3 = ggml_get_op_params_i32(dst, 7);
-
-    int64_t paddings[] = {lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3};
+    int64_t paddings[] = {
+        0, dst->ne[0] - src->ne[0], 0, dst->ne[1] - src->ne[1],
+        0, dst->ne[2] - src->ne[2], 0, dst->ne[3] - src->ne[3]};
     aclnn_pad(ctx, acl_src, acl_dst, paddings);
     ggml_cann_release_resources(ctx, acl_src, acl_dst);
 }
@@ -973,8 +964,8 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     }
     aclTensor* acl_gamma = get_f32_cache_acl_tensor(
         ctx,
-        &ctx.rms_norm_one_tensor_cache.cache,
-        ctx.rms_norm_one_tensor_cache.size,
+        &ctx.f32_one_cache,
+        ctx.f32_one_cache_element,
         src->ne,
         acl_gamma_nb,
         1,        // dims
@@ -982,19 +973,18 @@ void ggml_cann_rms_norm(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     );
 
     // build rstd, zero...
-    int64_t acl_rstd_ne[] = {src->ne[1], src->ne[2], src->ne[3]};
-    size_t acl_rstd_nb[GGML_MAX_DIMS - 1];
+    size_t acl_rstd_nb[GGML_MAX_DIMS];
     acl_rstd_nb[0] = sizeof(float);
-    for (int i = 1; i < GGML_MAX_DIMS - 1; i++) {
-        acl_rstd_nb[i] = acl_rstd_nb[i - 1] * acl_rstd_ne[i - 1];
+    for (int i = 1; i < GGML_MAX_DIMS; i++) {
+        acl_rstd_nb[i] = acl_rstd_nb[i - 1] * src->ne[i - 1];
     }
     aclTensor* acl_rstd = get_f32_cache_acl_tensor(
         ctx,
-        &ctx.rms_norm_zero_tensor_cache.cache,
-        ctx.rms_norm_zero_tensor_cache.size,
-        acl_rstd_ne,
+        &ctx.f32_zero_cache,
+        ctx.f32_zero_cache_element,
+        src->ne,
         acl_rstd_nb,
-        GGML_MAX_DIMS - 1,
+        GGML_MAX_DIMS,
         0.0f      // value
     );
 
@@ -1433,25 +1423,21 @@ static void aclnn_pow_tensor_tensor(ggml_backend_cann_context& ctx,
  * @param start         Starting exponent offset.
  * @param stop          Stopping exponent offset (exclusive).
  * @param step          Step size for the exponent increment.
- * @param dtype         Data type for slope tensor.
  */
 static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_buffer,
-    float m, int64_t size, float start, float stop, float step, ggml_type dtype){
-    aclDataType acl_type = ggml_cann_type_mapping(dtype);
-    size_t type_size = ggml_type_size(dtype);
-
+    float m, int64_t size, float start, float stop, float step){
     int64_t ne[] = {size};
-    size_t nb[] = {type_size};
+    size_t nb[] = {sizeof(uint16_t)};
 
-    ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * type_size);
+    ggml_cann_pool_alloc arange_allocator(ctx.pool(), size * sizeof(uint16_t));
     void* arange_buffer = arange_allocator.get();
 
     aclTensor* arange_tensor = ggml_cann_create_tensor(
-        arange_buffer, acl_type, type_size, ne, nb, 1);
+        arange_buffer, ACL_FLOAT16, sizeof(uint16_t), ne, nb, 1);
     aclnn_arange(ctx, arange_tensor, start, stop, step, size);
 
     aclTensor* slope_tensor = ggml_cann_create_tensor(
-        slope_buffer, acl_type, type_size, ne, nb, 1);
+        slope_buffer, ACL_FLOAT16, sizeof(uint16_t), ne, nb, 1);
 
     aclScalar* sc = aclCreateScalar(&m, aclDataType::ACL_FLOAT);
 
@@ -1482,11 +1468,10 @@ static void aclnn_get_slope_inner(ggml_backend_cann_context& ctx, void* slope_bu
  * @param n_head        Total number of attention heads.
  * @param slope_buffer  Pointer to the output buffer (float array) for storing slopes.
  * @param max_bias      Maximum bias value for slope computation.
- * @param dtype         Data type for slope tensor.
  *
 */
 static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head,
-    void* slope_buffer, float max_bias, ggml_type dtype) {
+    void* slope_buffer, float max_bias) {
     const int n_head_log2 = 1u << (uint32_t) floor(log2(n_head));
 
     float m0 = powf(2.0f, -(max_bias) / n_head_log2);
@@ -1503,7 +1488,7 @@ static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head,
     float step  = 1;
     float count = n_head_log2;
     // end needs to be +1 because aclnn uses a left-closed, right-open interval.
-    aclnn_get_slope_inner(ctx, slope_buffer, m0, count, start, end + 1, step, dtype);
+    aclnn_get_slope_inner(ctx, slope_buffer, m0, count, start, end + 1, step);
     if (n_head_log2 < n_head) {
         // arange2
         start = 2 * (n_head_log2 - n_head_log2) + 1;
@@ -1512,7 +1497,7 @@ static void aclnn_get_slope(ggml_backend_cann_context & ctx, int64_t n_head,
         count = n_head - n_head_log2;
         aclnn_get_slope_inner(
             ctx, (char *) slope_buffer + n_head_log2 * sizeof(float),
-            m1, count, start, end + 1, step, dtype);
+            m1, count, start, end + 1, step);
     }
 }
 
@@ -1549,7 +1534,7 @@ static void aclnn_add_alibi(ggml_backend_cann_context& ctx, ggml_tensor* mask,
         ggml_cann_pool_alloc bias_allocator(
                     ctx.pool(), ggml_nelements(dst) * ggml_element_size(dst));
         bias_buffer = bias_allocator.get();
-        aclnn_get_slope(ctx, n_heads, slope_buffer, max_bias, GGML_TYPE_F32);
+        aclnn_get_slope(ctx, n_heads, slope_buffer, max_bias);
     }
 
     // broadcast for mask, slop and dst;
@@ -1775,10 +1760,10 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         case GGML_TYPE_F16: {
             aclTensor* acl_src0 = ggml_cann_create_tensor(src0);
             ggml_cann_pool_alloc src_buffer_allocator(
-                ctx.pool(), ggml_nelements(src0) * sizeof(float));
+                ctx.pool(), ggml_nelements(src0) * sizeof(float_t));
             void* src_trans_buffer = src_buffer_allocator.get();
             size_t src_trans_nb[GGML_MAX_DIMS];
-            src_trans_nb[0] = sizeof(float);
+            src_trans_nb[0] = sizeof(float_t);
             for (int i = 1; i < GGML_MAX_DIMS; i++) {
                 src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
             }
@@ -1822,14 +1807,14 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
             // [3,4,5,64] -> [3,4,5,2,32]
             dequant_ne = weight_ne;
-            dequant_nb[0] = sizeof(float);
+            dequant_nb[0] = sizeof(float_t);
             for (int i = 1; i < GGML_MAX_DIMS + 1; i++) {
                 dequant_nb[i] = dequant_nb[i - 1] * dequant_ne[i - 1];
             }
 
             scale_offset = ggml_nelements(src0) * sizeof(int8_t);
             ggml_cann_pool_alloc dequant_buffer_allocator(
-                ctx.pool(), ggml_nelements(src0) * sizeof(float));
+                ctx.pool(), ggml_nelements(src0) * sizeof(float_t));
 
             aclTensor* acl_weight_tensor = ggml_cann_create_tensor(
                 src0->data, ACL_INT8, sizeof(int8_t), weight_ne, weight_nb,
@@ -1838,11 +1823,11 @@ void ggml_cann_get_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
                 src0->data, ACL_FLOAT16, sizeof(uint16_t), scale_ne, scale_nb,
                 GGML_MAX_DIMS + 1, ACL_FORMAT_ND, scale_offset);
             aclTensor* dequant_tensor = ggml_cann_create_tensor(
-                dequant_buffer_allocator.get(), ACL_FLOAT, sizeof(float),
+                dequant_buffer_allocator.get(), ACL_FLOAT, sizeof(float_t),
                 dequant_ne, dequant_nb, GGML_MAX_DIMS + 1);
 
             aclnn_mul(ctx, acl_weight_tensor, acl_scale_tensor, dequant_tensor);
-            dequant_nb[0] = sizeof(float);
+            dequant_nb[0] = sizeof(float_t);
             dequant_ne = src0->ne;
             for (int i = 1; i < GGML_MAX_DIMS; i++) {
                 dequant_nb[i] = dequant_nb[i - 1] * src0->ne[i - 1];
@@ -1963,7 +1948,7 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
     aclTensor* acl_weight_tensor;
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
     if (weight_to_nz && is_matmul_weight(weight)) {
         int64_t acl_stride[2] = {1, transpose_ne[1]};
 
@@ -2263,39 +2248,46 @@ static void aclnn_index_fill_tensor(ggml_backend_cann_context& ctx,
  *   5. Compute sin(θ), cos(θ) and optionally scale by attn_factor.
  *   6. Expand sin/cos values by repeat or repeat_interleave depending
  *      on whether @param is_neox is enabled.
+ *   7. Store the computed values into persistent buffers
+ *      (ctx.rope_sin_ptr / ctx.rope_cos_ptr).
  *
- * @param ctx                The CANN backend context, holding memory pool,
- *                           stream, and persistent buffers for rope init/cache.
- * @param dst                The destination ggml_tensor whose computation
- *                           depends on the RoPE values (usually Qcur/Kcur).
- * @param theta_scale        Scalar exponent base for computing theta scale values.
- * @param freq_scale         Frequency scaling factor, applied to theta scale.
- * @param attn_factor        Attention scaling factor, applied to sin/cos.
- * @param is_neox            Whether to use Neox-style repeat strategy
- *                           (dim expansion vs repeat_interleave).
+ * @param ctx         The CANN backend context, holding memory pool,
+ *                    stream, and persistent buffers for rope init/cache.
+ * @param dst         The destination ggml_tensor whose computation
+ *                    depends on the cached RoPE values (usually Qcur/Kcur).
+ * @param theta_scale Scalar exponent base for computing theta scale values.
+ * @param freq_scale  Frequency scaling factor, applied to theta scale.
+ * @param attn_factor Attention scaling factor, applied to sin/cos.
+ * @param is_neox     Whether to use Neox-style repeat strategy
+ *                    (dim expansion vs repeat_interleave).
  */
 static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
-                             float* corr_dims, float ext_factor,
                              float theta_scale, float freq_scale,
                              float attn_factor, bool is_neox) {
+    // int sin/cos cache, cache has different repeat method depond on
+    // @param.is_neox
+    bool is_q = (std::strncmp(dst->name, "Qcur-", 5) == 0);
+    bool is_k = (std::strncmp(dst->name, "Kcur-", 5) == 0);
+
+    // used for accuracy testing
+    bool is_attention = is_q || is_k;
+
+    // just compute in first layer in attention
+    bool is_fisrt_layer = (std::strncmp(dst->name, "Qcur-0", GGML_MAX_NAME) == 0);
+    if(is_attention && !is_fisrt_layer) {
+        return;
+    }
+
     ggml_tensor* src0 = dst->src[0];  // input
     ggml_tensor* src1 = dst->src[1];  // position
     ggml_tensor* src2 = dst->src[2];  // freq_factors
 
-    if(src2 == nullptr && ctx.rope_cache.cached
-        && ctx.rope_cache.ext_factor == ext_factor
-        && ctx.rope_cache.theta_scale == theta_scale
-        && ctx.rope_cache.freq_scale == freq_scale
-        && ctx.rope_cache.attn_factor == attn_factor
-        && ctx.rope_cache.is_neox == is_neox) {
-        // use cache.
-        return;
-    }
+    GGML_TENSOR_BINARY_OP_LOCALS
 
-    int64_t theta_scale_length = src0->ne[0] / 2;
+    int64_t theta_scale_length = ne00 / 2;
     int64_t theta_scale_ne[] = {theta_scale_length, 1, 1, 1};
-    size_t theta_scale_nb[] = {sizeof(float), sizeof(float), sizeof(float),
-                          theta_scale_length * sizeof(float)};
+    size_t theta_scale_nb[] = {sizeof(float_t), sizeof(float_t), sizeof(float_t),
+                          theta_scale_length * sizeof(float_t)};
 
     GGML_ASSERT(src1->type == GGML_TYPE_I32);
     int64_t position_length = src1->ne[0];
@@ -2305,127 +2297,65 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
 
     int64_t theta_ne[] = {theta_scale_length, 1, position_length, 1};
     size_t theta_nb[GGML_MAX_DIMS];
-    theta_nb[0] = sizeof(float);
+    theta_nb[0] = sizeof(float_t);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
         theta_nb[i] = theta_nb[i - 1] * theta_ne[i - 1];
     }
 
-    // theta_scale arange, [0,1,...,ne00/2 - 1]
-    aclTensor* acl_theta_scale_tensor = nullptr;
-    // cache theta scale
-    if (ctx.rope_cache.theta_scale_length != theta_scale_length ||
-        // theta_scale and freq_scale should not change during the current token inference process,
-        // so we can directly use == here instead of comparing the absolute difference.
-        ctx.rope_cache.theta_scale != theta_scale ||
-        ctx.rope_cache.freq_scale != freq_scale) {
-
-        ctx.rope_cache.theta_scale_length = theta_scale_length;
-
-        if (ctx.rope_cache.theta_scale_cache != nullptr) {
-            ACL_CHECK(aclrtFree(ctx.rope_cache.theta_scale_cache));
+    // init theta scale, just one time
+    if(ctx.rope_init_ptr == nullptr || !is_attention) {
+        // theta_scale arange, [0,1,...,ne00/2 - 1]
+        if(ctx.rope_init_ptr != nullptr){
+            ACL_CHECK(aclrtFree(ctx.rope_init_ptr));
         }
-        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST));
+        ACL_CHECK(aclrtMalloc(&ctx.rope_init_ptr, theta_scale_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
 
-        acl_theta_scale_tensor =
-            ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float),
+        aclTensor* acl_theta_scale_tensor =
+            ggml_cann_create_tensor(ctx.rope_init_ptr, ACL_FLOAT, sizeof(float_t),
                                     theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
-
         float start = 0;
         float step = 1;
-        float stop = theta_scale_length;
-        float n_elements = theta_scale_length;
+        float stop = ne00 / 2;
+        float n_elements = ne00 / 2;
         aclnn_arange(ctx, acl_theta_scale_tensor, start, stop, step, n_elements);
 
-        ggml_cann_pool_alloc yarn_ramp_allocator(ctx.pool());
-        aclTensor* acl_yarn_ramp_tensor = nullptr;
-        if (ext_factor != 0) {
-            // -rope_yarn_ramp
-            // const float y = (i0 / 2 - low) / MAX(0.001f, high - low);
-            // return MIN(1, MAX(0, y)) - 1;
-            yarn_ramp_allocator.alloc(theta_scale_length * sizeof(float));
-            void* yarn_ramp_buffer = yarn_ramp_allocator.get();
-            acl_yarn_ramp_tensor = ggml_cann_create_tensor(yarn_ramp_buffer, ACL_FLOAT, sizeof(float),
-                                           theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
-            float zero_value = 0, one_value = 1;
-            float denom_safe_value = MAX(0.001f, corr_dims[1] - corr_dims[0]);
-            aclScalar* low = aclCreateScalar(&corr_dims[0], aclDataType::ACL_FLOAT);
-            aclScalar* zero = aclCreateScalar(&zero_value, aclDataType::ACL_FLOAT);
-            aclScalar* one = aclCreateScalar(&one_value, aclDataType::ACL_FLOAT);
-            aclScalar* denom_safe = aclCreateScalar(&denom_safe_value, aclDataType::ACL_FLOAT);
-            aclScalar* ext_factor_sc = aclCreateScalar(&ext_factor, aclDataType::ACL_FLOAT);
-
-            GGML_CANN_CALL_ACLNN_OP(ctx, Subs, acl_theta_scale_tensor, low, one, acl_yarn_ramp_tensor);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceDivs, acl_yarn_ramp_tensor, denom_safe);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceThreshold, acl_yarn_ramp_tensor, zero, zero);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceClampMax, acl_yarn_ramp_tensor, one);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceSubs, acl_yarn_ramp_tensor, one, one);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor, ext_factor_sc);
-
-            // theta_interp = freq_scale * theta_extrap;
-            // theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-            // theta = freq_scale * theta_extrap * (1 - ramp_mix) + theta_extrap * ramp_mix;
-            // theta = freq_scale * theta_extrap - freq_scale * theta_extrap * ramp_mix + theta_extrap * ramp_mix;
-            // theta = theta_extrap * (freq_scale - freq_scale * ramp_mix + ramp_mix);
-            //
-            // we cache (freq_scale - freq_scale * ramp_mix + ramp_mix), Considering that the rope_yarn_ramp here is the inverse
-            // cache freq_scale + (freq_scale - 1) * ramp_mix
-            float freq_scale_1 = freq_scale - 1;
-            aclScalar* freq_scale_sc = aclCreateScalar(&freq_scale, aclDataType::ACL_FLOAT);
-            aclScalar* freq_scale_1_sc = aclCreateScalar(&freq_scale_1, aclDataType::ACL_FLOAT);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMuls, acl_yarn_ramp_tensor, freq_scale_1_sc);
-            GGML_CANN_CALL_ACLNN_OP(ctx, InplaceAdds, acl_yarn_ramp_tensor, freq_scale_sc, one);
-
-            ggml_cann_release_resources(ctx, low, zero, one, denom_safe, ext_factor_sc, freq_scale_sc, freq_scale_1_sc);
-        }
-
         // power
         aclScalar* acl_theta_scale = aclCreateScalar(&theta_scale, aclDataType::ACL_FLOAT);
         GGML_CANN_CALL_ACLNN_OP(ctx, PowScalarTensor, acl_theta_scale, acl_theta_scale_tensor,
                                 acl_theta_scale_tensor);
 
-        if (ext_factor != 0) {
-            aclnn_mul(ctx, acl_theta_scale_tensor, acl_yarn_ramp_tensor);
-        } else if (freq_scale != 1) {
+        // freq_scale
+        if (freq_scale != 1) {
             aclnn_muls(ctx, acl_theta_scale_tensor, freq_scale, nullptr, true);
         }
 
-        ggml_cann_release_resources(ctx, acl_yarn_ramp_tensor, acl_theta_scale);
-    } else {
-        // use cache
-        acl_theta_scale_tensor =
-            ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float),
+        // freq_factors
+        if (src2) {
+            aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor(
+                src2->data, ggml_cann_type_mapping(src2->type),
+                ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
+            aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor);
+            ggml_cann_release_resources(ctx, acl_freq_factors_tensor);
+        }
+        // release
+        ggml_cann_release_resources(ctx, acl_theta_scale_tensor,acl_theta_scale);
+    }
+
+    // init sin_repeat && cos_repeat, one token just init in 0 layer
+    if(position_length > ctx.max_prompt_length) {
+        ctx.max_prompt_length = position_length;
+        int64_t repeat_theta_length = theta_scale_length * ctx.max_prompt_length * 2;
+        if(ctx.rope_sin_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(ctx.rope_sin_ptr));
+            ACL_CHECK(aclrtFree(ctx.rope_cos_ptr));
+        }
+        ACL_CHECK(aclrtMalloc(&ctx.rope_sin_ptr, repeat_theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+        ACL_CHECK(aclrtMalloc(&ctx.rope_cos_ptr, repeat_theta_length * sizeof(float_t), ACL_MEM_MALLOC_HUGE_FIRST));
+    }
+
+    aclTensor* acl_theta_scale_tensor =
+            ggml_cann_create_tensor(ctx.rope_init_ptr, ACL_FLOAT, sizeof(float_t),
                                     theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
-    }
-
-    ggml_cann_pool_alloc freq_fac_res_allocator(ctx.pool());
-    // freq_factors
-    if (src2) {
-        freq_fac_res_allocator.alloc(theta_scale_length * sizeof(float));
-        void* freq_fac_res_ptr = freq_fac_res_allocator.get();
-        aclTensor* acl_freq_factors_tensor = ggml_cann_create_tensor(
-            src2->data, ggml_cann_type_mapping(src2->type),
-            ggml_type_size(src2->type), theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
-        aclTensor* acl_freq_fac_res_tensor = ggml_cann_create_tensor(
-            freq_fac_res_ptr, ACL_FLOAT, sizeof(float),
-            theta_scale_ne, theta_scale_nb, GGML_MAX_DIMS);
-        aclnn_div(ctx, acl_theta_scale_tensor, acl_freq_factors_tensor, acl_freq_fac_res_tensor);
-        std::swap(acl_theta_scale_tensor, acl_freq_fac_res_tensor);
-        ggml_cann_release_resources(ctx, acl_freq_factors_tensor, acl_freq_fac_res_tensor);
-    }
-
-    // init sin_repeat && cos_repeat, only to accelerate first layer on each device
-    if (position_length > ctx.rope_cache.position_length) {
-        ctx.rope_cache.position_length = position_length;
-        if (ctx.rope_cache.sin_cache != nullptr) {
-            ACL_CHECK(aclrtFree(ctx.rope_cache.sin_cache));
-        }
-        if (ctx.rope_cache.cos_cache != nullptr) {
-            ACL_CHECK(aclrtFree(ctx.rope_cache.cos_cache));
-        }
-        int64_t repeat_theta_length = theta_scale_length * position_length * 2;
-        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.sin_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST));
-        ACL_CHECK(aclrtMalloc(&ctx.rope_cache.cos_cache, repeat_theta_length * sizeof(float), ACL_MEM_MALLOC_HUGE_FIRST));
-    }
 
     // position
     aclTensor* acl_position_tensor = ggml_cann_create_tensor(
@@ -2435,53 +2365,49 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
     // power * position
     int64_t theta_length = theta_scale_length * position_length;
     ggml_cann_pool_alloc theta_allocator(ctx.pool(),
-                                        theta_length * sizeof(float));
+                                        theta_length * sizeof(float_t));
     void* theta_buffer = theta_allocator.get();
 
     aclTensor* acl_theta_tensor =
-        ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float),
+        ggml_cann_create_tensor(theta_buffer, ACL_FLOAT, sizeof(float_t),
                                 theta_ne, theta_nb, GGML_MAX_DIMS);
     aclnn_mul(ctx, acl_position_tensor, acl_theta_scale_tensor,
             acl_theta_tensor);
 
     // sin/cos
     ggml_cann_pool_alloc sin_allocator(ctx.pool(),
-                                    theta_length * sizeof(float));
+                                    theta_length * sizeof(float_t));
     void* sin_buffer = sin_allocator.get();
     aclTensor* acl_sin_tensor = ggml_cann_create_tensor(
-        sin_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb,
+        sin_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
         GGML_MAX_DIMS, ACL_FORMAT_ND);
     aclnn_sin(ctx, acl_theta_tensor, acl_sin_tensor);
 
     ggml_cann_pool_alloc cos_allocator(ctx.pool(),
-                                    theta_length * sizeof(float));
+                                    theta_length * sizeof(float_t));
     void* cos_buffer = cos_allocator.get();
     aclTensor* acl_cos_tensor = ggml_cann_create_tensor(
-        cos_buffer, ACL_FLOAT, sizeof(float), theta_ne, theta_nb,
+        cos_buffer, ACL_FLOAT, sizeof(float_t), theta_ne, theta_nb,
         GGML_MAX_DIMS, ACL_FORMAT_ND);
     aclnn_cos(ctx, acl_theta_tensor, acl_cos_tensor);
 
-    if (ext_factor != 0) {
-        attn_factor *= 1.0f + 0.1f * logf(1.0f / freq_scale);
-    }
-
     // attn_factor
     if (attn_factor != 1) {
         aclnn_muls(ctx, acl_sin_tensor, attn_factor, nullptr, true);
         aclnn_muls(ctx, acl_cos_tensor, attn_factor, nullptr, true);
     }
 
-    int64_t sin_reshape_ne[4] = {src0->ne[0], 1, src0->ne[2], 1};
+    int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
     size_t sin_reshape_nb[GGML_MAX_DIMS];
-    sin_reshape_nb[0] = sizeof(float);
+    sin_reshape_nb[0] = sizeof(float_t);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
         sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
     }
     aclTensor* acl_sin_repeat_tensor =
-        ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float),
+        ggml_cann_create_tensor(ctx.rope_sin_ptr, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
     aclTensor* acl_cos_repeat_tensor =
-        ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float),
+        ggml_cann_create_tensor(ctx.rope_cos_ptr, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
 
     // repeat
@@ -2499,14 +2425,6 @@ static void aclnn_cache_init(ggml_backend_cann_context& ctx, ggml_tensor* dst,
                                 num_repeats, output_size);
     }
 
-    // Other layers use cache except first layer.
-    ctx.rope_cache.cached = true;
-    ctx.rope_cache.ext_factor = ext_factor;
-    ctx.rope_cache.theta_scale = theta_scale;
-    ctx.rope_cache.freq_scale = freq_scale;
-    ctx.rope_cache.attn_factor = attn_factor;
-    ctx.rope_cache.is_neox = is_neox;
-
     ggml_cann_release_resources(ctx, acl_theta_scale_tensor, acl_position_tensor,
         acl_theta_tensor, acl_sin_tensor, acl_sin_repeat_tensor, acl_cos_tensor,
         acl_cos_repeat_tensor);
@@ -2528,6 +2446,8 @@ aclnnStatus aclnnRotaryPositionEmbedding(void* workspace,
 #endif
 
 void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
+    // TODO: use ascendc
+    // Only test with LLAMA model.
     ggml_tensor* src0 = dst->src[0];  // input
 
     // param
@@ -2550,6 +2470,8 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     // TODO: n_dims <= ne0
     GGML_ASSERT(n_dims == ne0);
     GGML_ASSERT(n_dims % 2 == 0);
+    // TODO: ext_factor != 0
+    GGML_ASSERT(ext_factor == 0);
 
     const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
@@ -2560,20 +2482,19 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
 
     // init ctx.rope_cos/rope_sin cache
-    aclnn_cache_init(ctx, dst, corr_dims, ext_factor,
-                    theta_scale, freq_scale, attn_factor, is_neox);
+    aclnn_cache_init(ctx, dst, theta_scale, freq_scale, attn_factor, is_neox);
 
     int64_t sin_reshape_ne[4] = {ne00, 1, ne02, 1};
     size_t sin_reshape_nb[GGML_MAX_DIMS];
-    sin_reshape_nb[0] = sizeof(float);
+    sin_reshape_nb[0] = sizeof(float_t);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
         sin_reshape_nb[i] = sin_reshape_nb[i - 1] * sin_reshape_ne[i - 1];
     }
     aclTensor* acl_sin_reshape_tensor =
-        ggml_cann_create_tensor(ctx.rope_cache.sin_cache, ACL_FLOAT, sizeof(float),
+        ggml_cann_create_tensor(ctx.rope_sin_ptr, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
     aclTensor* acl_cos_reshape_tensor =
-        ggml_cann_create_tensor(ctx.rope_cache.cos_cache, ACL_FLOAT, sizeof(float),
+        ggml_cann_create_tensor(ctx.rope_cos_ptr, ACL_FLOAT, sizeof(float_t),
                                 sin_reshape_ne, sin_reshape_nb, GGML_MAX_DIMS);
 
     aclTensor* acl_src = ggml_cann_create_tensor(src0);
@@ -2588,7 +2509,7 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     void* minus_one_scale_buffer = nullptr;
     ggml_cann_pool_alloc roll_allocator(ctx.pool(), ggml_nbytes(src0));
     ggml_cann_pool_alloc minus_one_scale_allocator(
-        ctx.pool(), sizeof(float) * src0->ne[0]);
+        ctx.pool(), sizeof(float_t) * src0->ne[0]);
     if (!is_neox) {
         // roll input: [q0,q1,q2,q3,...] -> [q1,q0,q3,q2,...]
         input_roll_buffer = roll_allocator.get();
@@ -2618,13 +2539,13 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
 
         int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1};
         size_t minus_one_nb[GGML_MAX_DIMS];
-        minus_one_nb[0] = sizeof(float);
+        minus_one_nb[0] = sizeof(float_t);
         for (int i = 1; i < GGML_MAX_DIMS; i++) {
             minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
         }
         acl_minus_one_tensor = aclnn_values(
-            ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0],
-            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1);
+            ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0],
+            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1);
         int64_t dim = 3;
         int64_t* index = new int64_t[src0->ne[0]];
         for (int i = 0; i < src0->ne[0]; i++) {
@@ -2652,22 +2573,22 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         minus_one_scale_buffer = minus_one_scale_allocator.get();
         int64_t minus_one_ne[4] = {src0->ne[0], 1, 1, 1};
         size_t minus_one_nb[GGML_MAX_DIMS];
-        minus_one_nb[0] = sizeof(float);
+        minus_one_nb[0] = sizeof(float_t);
         for (int i = 1; i < GGML_MAX_DIMS; i++) {
             minus_one_nb[i] = minus_one_nb[i - 1] * minus_one_ne[i - 1];
         }
         acl_minus_one_tensor = aclnn_values(
-            ctx, minus_one_scale_buffer, sizeof(float) * src0->ne[0],
-            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float), 1);
+            ctx, minus_one_scale_buffer, sizeof(float_t) * src0->ne[0],
+            minus_one_ne, GGML_MAX_DIMS, ACL_FLOAT, sizeof(float_t), 1);
         // -1 * first half
         int64_t first_half_ne[4] = {src0->ne[0] / 2, 1, 1, 1};
         size_t first_half_nb[GGML_MAX_DIMS];
-        first_half_nb[0] = sizeof(float);
+        first_half_nb[0] = sizeof(float_t);
         for (int i = 1; i < GGML_MAX_DIMS; i++) {
             first_half_nb[i] = first_half_nb[i - 1] * first_half_ne[i - 1];
         }
         aclTensor* acl_first_half_tensor = ggml_cann_create_tensor(
-            minus_one_scale_buffer, ACL_FLOAT, sizeof(float), first_half_ne,
+            minus_one_scale_buffer, ACL_FLOAT, sizeof(float_t), first_half_ne,
             first_half_nb, GGML_MAX_DIMS);
         bool inplace = true;
         float scale = -1;
@@ -2707,28 +2628,28 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
         // TODO: ne0 != n_dims in mode2
     } else if (src0->type == GGML_TYPE_F16) {
         size_t input_fp32_nb[GGML_MAX_DIMS];
-        input_fp32_nb[0] = sizeof(float);
+        input_fp32_nb[0] = sizeof(float_t);
         for (int i = 1; i < GGML_MAX_DIMS; i++) {
             input_fp32_nb[i] = input_fp32_nb[i - 1] * dst->ne[i - 1];
         }
         ggml_cann_pool_alloc fp32_allocator1(
-            ctx.pool(), ggml_nelements(dst) * sizeof(float));
+            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
         void* input_fp32_buffer1 = fp32_allocator1.get();
         aclTensor* input_fp32_tensor1 = ggml_cann_create_tensor(
-            input_fp32_buffer1, ACL_FLOAT, sizeof(float), dst->ne,
+            input_fp32_buffer1, ACL_FLOAT, sizeof(float_t), dst->ne,
             input_fp32_nb, GGML_MAX_DIMS);
         ggml_cann_pool_alloc fp32_allocator2(
-            ctx.pool(), ggml_nelements(dst) * sizeof(float));
+            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
         void* input_fp32_buffer2 = fp32_allocator2.get();
         aclTensor* input_fp32_tensor2 = ggml_cann_create_tensor(
-            input_fp32_buffer2, ACL_FLOAT, sizeof(float), dst->ne,
+            input_fp32_buffer2, ACL_FLOAT, sizeof(float_t), dst->ne,
             input_fp32_nb, GGML_MAX_DIMS);
 
         ggml_cann_pool_alloc fp32_allocator(
-            ctx.pool(), ggml_nelements(dst) * sizeof(float));
+            ctx.pool(), ggml_nelements(dst) * sizeof(float_t));
         output_fp32_buffer = fp32_allocator.get();
         aclTensor* output_fp32_tensor = ggml_cann_create_tensor(
-            output_fp32_buffer, ACL_FLOAT, sizeof(float), dst->ne,
+            output_fp32_buffer, ACL_FLOAT, sizeof(float_t), dst->ne,
             input_fp32_nb, GGML_MAX_DIMS);
         aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor, input_fp32_tensor1);
         aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor,
@@ -2825,6 +2746,8 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* ds
     aclIntArray *padding = aclCreateIntArray(paddingVal, 1);
     int64_t dilationVal[] = {1};
     aclIntArray *dilation = aclCreateIntArray(dilationVal, 1);
+    bool transposed = true;
+    int64_t groups = 1;
     int8_t cubeMathType = 0;
 
 #ifdef ASCEND_310P
@@ -2832,7 +2755,7 @@ void ggml_cann_conv_transpose_1d(ggml_backend_cann_context& ctx, ggml_tensor* ds
 #endif
 
     GGML_CANN_CALL_ACLNN_OP(ctx, Convolution, acl_input, acl_weight, nullptr, stride,
-        padding, dilation, true, padding, 1, acl_dst, cubeMathType);
+        padding, dilation, transposed, padding, groups, acl_dst, cubeMathType);
 
     ggml_cann_release_resources(ctx, acl_weight, acl_dst, stride, padding, dilation);
 }
@@ -2941,49 +2864,174 @@ void ggml_cann_step(ggml_backend_cann_context& ctx, ggml_tensor* dst){
  */
 static void ggml_cann_mul_mat_id_fp(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
     //dst   [M, K, N, 1]
-    ggml_tensor * src0 = dst->src[0];  //src0	[D, M, A, 1]  -> [D, M, K, 1]
-    ggml_tensor * src1 = dst->src[1];  //src1	[D, B, N, 1], B = K or B = 1 -> [D, 1, K, 1]
+    ggml_tensor * src0 = dst->src[0];  //src0	[D, M, A, 1]
+    ggml_tensor * src1 = dst->src[1];  //src1	[D, B, N, 1], B = K or B = 1
     ggml_tensor * ids  = dst->src[2];  //ids	[K, N]
 
-    GGML_ASSERT(src0->ne[3] == 1);
-    GGML_ASSERT(src1->ne[3] == 1);
-    GGML_ASSERT(dst->ne[3] == 1);
+    GGML_TENSOR_BINARY_OP_LOCALS
 
-    int64_t batch = src1->ne[2];
-    GGML_ASSERT(batch == ids->ne[1]);
+    // copy index from npu to cpu
+    int64_t n_as = ne02; // A
+    int64_t n_ids = ids->ne[0]; // K
 
-    ggml_cann_pool_alloc export_allocator(ctx.pool(), src0->ne[0] * src0->ne[1] * ids->ne[0] * ggml_element_size(src0));
-    void* export_ptr = export_allocator.get();
-    for (int64_t i = 0; i < batch; i++) {
-        aclTensor *select_index = ggml_cann_create_tensor(ids, ids->ne, ids->nb, 1, ACL_FORMAT_ND, i * ids->nb[1]);
-        aclTensor *export_weight = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3);
+    std::vector<char> ids_host(ggml_nbytes(ids));
+    ggml_cann_async_memcpy(ctx, ids_host.data(), ids->data, ggml_nbytes(ids),
+        ACL_MEMCPY_DEVICE_TO_HOST);
+    ACL_CHECK(aclrtSynchronizeStream(ctx.stream()));
 
-        int64_t select_export_ne[] = {src0->ne[0], src0->ne[1], ids->ne[0]};
-        size_t select_export_nb[3];
-        select_export_nb[0] = src0->nb[0];
-        for (int k = 1;k < 3; k++) {
-            select_export_nb[k] = select_export_nb[k-1] * select_export_ne[k-1];
+    char * src0_original = (char *) src0->data;
+    char * src1_original = (char *) src1->data;
+    char * dst_original  = (char *)  dst->data;
+    size_t ori_src0_nb[4] = {nb00, nb01, nb02, nb03};
+
+    // src0 is F16, src1 is F32, dst is F32
+    ggml_cann_pool_alloc src0_cast_allocator;
+    if (src0->type == GGML_TYPE_F16) {
+        src0_cast_allocator.alloc(ctx.pool(), sizeof(float) * ggml_nelements(src0));
+        void* src0_cast_buf = src0_cast_allocator.get();
+
+        size_t cast_nb[GGML_MAX_DIMS];
+        cast_nb[0] = sizeof(float_t);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            cast_nb[i] = cast_nb[i - 1] * src0->ne[i - 1];
         }
 
-        aclTensor *select_export = ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), select_export_ne, select_export_nb, 3);
-        GGML_CANN_CALL_ACLNN_OP(ctx, IndexSelect, export_weight, 0, select_index, select_export);
+        aclTensor* acl_src0_f16 = ggml_cann_create_tensor(src0);
+        aclTensor* acl_cast = ggml_cann_create_tensor(src0_cast_buf,
+            ACL_FLOAT, sizeof(float), src0->ne, cast_nb, 4);
+        GGML_CANN_CALL_ACLNN_OP(ctx, Cast, acl_src0_f16, ACL_FLOAT, acl_cast);
+        ggml_cann_release_resources(ctx, acl_cast, acl_src0_f16);
 
-        int64_t select_transpose_ne[] = {select_export_ne[1], select_export_ne[0], select_export_ne[2]};
-        size_t select_transpose_nb[] = {select_export_nb[1], select_export_nb[0], select_export_nb[2]};
-        aclTensor *select_export_transpose = ggml_cann_create_tensor(export_ptr, ggml_cann_type_mapping(src0->type), ggml_element_size(src0), select_transpose_ne, select_transpose_nb, 3);
-
-        int64_t active_tensor_ne[] = {src1->ne[0], 1, src1->ne[1]};
-        size_t active_tensor_nb[] = {src1->nb[0], src1->nb[1], src1->nb[1]};
-        aclTensor *active_tensor = ggml_cann_create_tensor(src1, active_tensor_ne, active_tensor_nb, 3, ACL_FORMAT_ND, i * src1->nb[2]);
-
-        int64_t dst_ne[] = {dst->ne[0], 1, dst->ne[1]};
-        size_t dst_nb[] = {dst->nb[0], dst->nb[1], dst->nb[1]};
-        aclTensor *acl_dst = ggml_cann_create_tensor(dst, dst_ne,dst_nb, 3, ACL_FORMAT_ND, i * dst->nb[2]);
-
-        GGML_CANN_CALL_ACLNN_OP(ctx, BatchMatMul, active_tensor, select_export_transpose, acl_dst, 2);
-
-        ggml_cann_release_resources(ctx, select_index, export_weight, select_export, active_tensor, acl_dst, select_export_transpose);
+        src0_original = (char *) src0_cast_buf;
+        memcpy(ori_src0_nb, cast_nb, sizeof(ori_src0_nb));
     }
+
+#ifdef ASCEND_310P
+    ggml_tensor src0_row = *src0;
+    ggml_tensor src1_row = *src1;
+    ggml_tensor dst_row = *dst;
+
+    if (src0->type == GGML_TYPE_F16) {
+        src0_row.type = GGML_TYPE_F32;
+    }
+
+    // src0_row [D, M, 1, 1] weight without permute
+    src0_row.ne[2] = 1;
+    src0_row.ne[3] = 1;
+    src0_row.nb[0] = ori_src0_nb[0];
+    src0_row.nb[1] = ori_src0_nb[1];
+    src0_row.nb[2] = ori_src0_nb[1];
+    src0_row.nb[3] = ori_src0_nb[1];
+
+    // src1_row [D, 1, 1, 1] -> input
+    src1_row.ne[1] = 1;
+    src1_row.ne[2] = 1;
+    src1_row.ne[3] = 1;
+    src1_row.nb[2] = nb11;
+    src1_row.nb[3] = nb11;
+
+    // dst_row [M, 1, 1, 1] -> out
+    dst_row.ne[1] = 1;
+    dst_row.ne[2] = 1;
+    dst_row.ne[3] = 1;
+    dst_row.nb[2] = nb1;
+    dst_row.nb[3] = nb1;
+
+    //create weight for one row
+    for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+        for (int64_t id = 0; id < n_ids; id++) {
+            // expert index
+            int32_t i02 = *(int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
+            GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+            // If B = 1 (broadcast), always use 0; otherwise, use id.
+            int64_t i11 = (ne11 == 1 ? 0 : id);
+            int64_t i12 = iid1;
+
+            int64_t i1 = id;
+            int64_t i2 = i12;
+
+            void* src0_tmp_ptr = src0_original + i02*ori_src0_nb[2];
+            void* src1_tmp_ptr = src1_original + i11*nb11 + i12*nb12;
+            void* dst_tmp_ptr  = dst_original  + i1*nb1   + i2*nb2;
+
+            src0_row.data = src0_tmp_ptr;
+            src1_row.data = src1_tmp_ptr;
+            dst_row.data = dst_tmp_ptr;
+            dst_row.src[0] = &src0_row;
+            dst_row.src[1] = &src1_row;
+
+            ggml_cann_mul_mat(ctx, &dst_row);
+        }
+    }
+    return;
+#endif
+
+    std::vector<aclTensor*> src0_tensor_vec;
+    std::vector<aclTensor*> src1_tensor_vec;
+    std::vector<aclTensor*> dst_tensor_vec;
+    for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) {
+        for (int64_t id = 0; id < n_ids; id++) {
+            // src0_row [M, D] -> weight && permute
+            int64_t src0_ne[2] = {ne01, ne00};
+            size_t src0_nb[2] = {ori_src0_nb[1], ori_src0_nb[0]};
+            // src1_row [D, 1] -> input
+            int64_t src1_ne[2] = {ne10, 1};
+            size_t src1_nb[2] = {nb10, nb11};
+            // dst_row [M, 1] -> out
+            int64_t dst_ne[2] = {ne0, 1};
+            size_t dst_nb[2] = {nb0, nb1};
+
+            // expert index
+            int32_t i02 = *(int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]);
+            GGML_ASSERT(i02 >= 0 && i02 < n_as);
+
+            // If B = 1 (broadcast), always use 0; otherwise, use id.
+            int64_t i11 = (ne11 == 1 ? 0 : id);
+            int64_t i12 = iid1;
+
+            int64_t i1 = id;
+            int64_t i2 = i12;
+
+            void* src0_tmp_ptr = src0_original + i02*ori_src0_nb[2];
+            void* src1_tmp_ptr = src1_original + i11*nb11 + i12*nb12;
+            void* dst_tmp_ptr  = dst_original  + i1*nb1   + i2*nb2;
+
+            aclTensor* acl_src0 = ggml_cann_create_tensor(src0_tmp_ptr,
+                ACL_FLOAT, sizeof(float),
+                src0_ne, src0_nb, 2);
+            aclTensor* acl_src1 = ggml_cann_create_tensor(src1_tmp_ptr,
+                ACL_FLOAT, sizeof(float),
+                src1_ne, src1_nb, 2);
+            aclTensor* acl_dst = ggml_cann_create_tensor(dst_tmp_ptr,
+                ACL_FLOAT, sizeof(float),
+                dst_ne, dst_nb, 2);
+
+            src0_tensor_vec.push_back(acl_src0);
+            src1_tensor_vec.push_back(acl_src1);
+            dst_tensor_vec.push_back(acl_dst);
+        }
+    }
+
+    size_t GROUP_SIZE = 128;
+    // GroupedMatmulV3 required tensor_list.size < 128
+    for (size_t i = 0; i < src0_tensor_vec.size(); i += GROUP_SIZE) {
+        // split and call GroupedMatmulV3
+        size_t end = std::min(i + GROUP_SIZE, src0_tensor_vec.size());
+        std::vector<aclTensor*> src0_tensor_vec_split(src0_tensor_vec.begin() + i, src0_tensor_vec.begin() + end);
+        std::vector<aclTensor*> src1_tensor_vec_split(src1_tensor_vec.begin() + i, src1_tensor_vec.begin() + end);
+        std::vector<aclTensor*> dst_tensor_vec_split(dst_tensor_vec.begin() + i, dst_tensor_vec.begin() + end);
+
+        aclTensorList* src0_tensor_list = aclCreateTensorList(src0_tensor_vec_split.data(), src0_tensor_vec_split.size());
+        aclTensorList* src1_tensor_list = aclCreateTensorList(src1_tensor_vec_split.data(), src1_tensor_vec_split.size());
+        aclTensorList* dst_tensor_list = aclCreateTensorList(dst_tensor_vec_split.data(), dst_tensor_vec_split.size());
+
+        GGML_CANN_CALL_ACLNN_OP(ctx, GroupedMatmulV3, src1_tensor_list, src0_tensor_list,
+            nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, -1, dst_tensor_list);
+
+        ggml_cann_release_resources(ctx, src0_tensor_list, src1_tensor_list, dst_tensor_list);
+    }
+    return;
 }
 
 /**
@@ -3294,7 +3342,7 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context& ctx, ggml_tensor* dst){
                 const int64_t n_heads = src0->ne[2];
                 ggml_cann_pool_alloc slope_allocator(ctx.pool(), n_heads * sizeof(uint16_t));
                 void* slope_buffer = slope_allocator.get();
-                aclnn_get_slope(ctx, n_heads, slope_buffer, maxBias, GGML_TYPE_F16);
+                aclnn_get_slope(ctx, n_heads, slope_buffer, maxBias);
 
                 int64_t slope_ne[] = {1, 1, n_heads, 1};
                 size_t slope_nb[GGML_MAX_DIMS];

ggml/src/ggml-cann/common.h

@@ -38,7 +38,6 @@
 #include <unistd.h>
 #include <functional>
 #include <optional>
-#include <list>
 
 #include "../include/ggml-cann.h"
 #include "../include/ggml.h"
@@ -107,7 +106,6 @@ int32_t ggml_cann_get_device();
 
 std::optional<std::string> get_env(const std::string& name);
 bool parse_bool(const std::string& value);
-int parse_integer(const std::string& value);
 
 /**
  * @brief Abstract base class for memory pools used by CANN.
@@ -352,7 +350,7 @@ struct ggml_graph_node_properties {
 struct ggml_cann_graph {
     ~ggml_cann_graph() {
         if (graph != nullptr) {
-            ACL_CHECK(aclmdlRIDestroy(graph));
+            aclmdlRIDestroy(graph);
         }
     }
 
@@ -360,105 +358,8 @@ struct ggml_cann_graph {
 
     std::vector<ggml_graph_node_properties> ggml_graph_properties;
 };
-
-/**
- * @brief LRU cache for managing ggml_cann_graph objects.
- *
- * This class maintains a list of shared_ptr to ggml_cann_graph objects
- * and enforces a maximum capacity. It provides methods to push new graphs,
- * move existing graphs to the front (most recently used), and clear the cache.
- */
-struct ggml_cann_graph_lru_cache {
-    size_t capacity;  /**< Maximum number of graphs in the 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("GGML_CANN_GRAPH_CACHE_CAPACITY").value_or("12"));
-    }
-
-    /**
-     * @brief Push a new graph to the front of the cache.
-     * If the cache exceeds capacity, the least recently used graph is deleted.
-     * @param new_node Pointer to the new ggml_cann_graph to cache.
-     *        Ownership is transferred to the cache (cache will delete it).
-     */
-    void push(ggml_cann_graph* new_node) {
-        if (cache_list.size() >= capacity) {
-            ggml_cann_graph* old = cache_list.back();
-            cache_list.pop_back();
-            delete old; // free the old graph
-        }
-        cache_list.push_front(new_node);
-    }
-
-    /**
-     * @brief Move an existing graph to the front of the cache.
-     * @param node Pointer to the ggml_cann_graph to move.
-     */
-    void move_to_front(ggml_cann_graph* node) {
-        cache_list.remove(node);
-        cache_list.push_front(node);
-    }
-
-    /**
-     * @brief Clear all graphs from the cache (also frees memory).
-     */
-    void clear() {
-        for (auto ptr : cache_list) {
-            delete ptr;
-        }
-        cache_list.clear();
-    }
-
-    /**
-     * @brief Destructor that clears the cache and frees all cached graphs.
-     */
-    ~ggml_cann_graph_lru_cache() {
-        clear();
-    }
-};
 #endif  // USE_ACL_GRAPH
 
-struct ggml_cann_rope_cache {
-    ~ggml_cann_rope_cache() {
-        if(theta_scale_cache != nullptr) {
-            ACL_CHECK(aclrtFree(theta_scale_cache));
-        }
-        if(sin_cache != nullptr) {
-            ACL_CHECK(aclrtFree(sin_cache));
-        }
-        if(cos_cache != nullptr) {
-            ACL_CHECK(aclrtFree(cos_cache));
-        }
-    }
-
-    void* theta_scale_cache = nullptr;
-    int64_t theta_scale_length = 0;
-    // sin/cos cache, used only to accelerate first layer on each device
-    void* sin_cache = nullptr;
-    void* cos_cache = nullptr;
-    int64_t position_length = 0;
-    // Properties to check before reusing the sincos cache
-    bool cached = false;
-    float ext_factor = 0.0f;
-    float theta_scale = 0.0f;
-    float freq_scale = 0.0f;
-    float attn_factor = 0.0f;
-    bool is_neox = false;
-};
-
-struct ggml_cann_tensor_cache {
-    ~ggml_cann_tensor_cache() {
-        if(cache != nullptr) {
-            ACL_CHECK(aclrtFree(cache));
-        }
-    }
-
-    void* cache = nullptr;
-    int64_t size = 0;
-};
-
 /**
  * @brief Context for managing CANN backend operations.
  */
@@ -469,16 +370,20 @@ struct ggml_backend_cann_context {
     aclrtEvent copy_event = nullptr; /**< Event for managing copy operations. */
 #ifdef USE_ACL_GRAPH
     /// Cached CANN ACL graph used for executing the current ggml computation graph.
-    ggml_cann_graph_lru_cache graph_lru_cache;
-    bool acl_graph_mode = true;
+    std::unique_ptr<ggml_cann_graph> cann_graph;
 #endif
     cann_task_queue task_queue;
     bool async_mode;
     // Rope Cache
-    ggml_cann_rope_cache rope_cache;
+    void* rope_init_ptr = nullptr;
+    void* rope_sin_ptr = nullptr;
+    void* rope_cos_ptr = nullptr;
+    int64_t max_prompt_length = 0;
     // Constant Pool
-    ggml_cann_tensor_cache rms_norm_one_tensor_cache;
-    ggml_cann_tensor_cache rms_norm_zero_tensor_cache;
+    void* f32_zero_cache = nullptr;
+    void* f32_one_cache = nullptr;
+    int64_t f32_zero_cache_element = 0;
+    int64_t f32_one_cache_element = 0;
 
     aclrtStream streams[GGML_CANN_MAX_STREAMS] = {nullptr}; /**< Array of streams for the device. */
 
@@ -494,13 +399,6 @@ struct ggml_backend_cann_context {
         async_mode = parse_bool(get_env("GGML_CANN_ASYNC_MODE").value_or(""));
         GGML_LOG_INFO("%s: device %d async operator submission is %s\n", __func__,
             device, async_mode ? "ON" : "OFF");
-#ifdef USE_ACL_GRAPH
-        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
     }
 
     /**
@@ -517,6 +415,21 @@ struct ggml_backend_cann_context {
                 ACL_CHECK(aclrtDestroyStream(streams[i]));
             }
         }
+        if(rope_init_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(rope_init_ptr));
+        }
+        if(rope_sin_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(rope_sin_ptr));
+        }
+        if(rope_cos_ptr != nullptr) {
+            ACL_CHECK(aclrtFree(rope_cos_ptr));
+        }
+        if(f32_zero_cache != nullptr) {
+            ACL_CHECK(aclrtFree(f32_zero_cache));
+        }
+        if(f32_one_cache != nullptr) {
+            ACL_CHECK(aclrtFree(f32_one_cache));
+        }
     }
 
     /**
@@ -526,10 +439,7 @@ struct ggml_backend_cann_context {
      */
     aclrtStream stream(int stream) {
         if (streams[stream] == nullptr) {
-            // If the device is not set here, destroying the stream later may cause a mismatch
-            // between the thread contexts where the stream was created and destroyed.
-            // However, I printed the device_id, thread_id, and stream, and they are all consistent.
-            ACL_CHECK(aclrtSetDevice(device));
+            ggml_cann_set_device(device);
             ACL_CHECK(aclrtCreateStream(&streams[stream]));
         }
         return streams[stream];

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

@@ -75,12 +75,13 @@
  * @param device The device ID to set.
  */
 void ggml_cann_set_device(const int32_t device) {
-    int current_device = -1;
-    aclrtGetDevice(&current_device);
+    // TODO: uncomment these lines after empty context has fixed.
+    // int current_device;
+    // ACL_CHECK(aclrtGetDevice(&current_device));
 
-    if (device == current_device) {
-      return;
-    }
+    // if (device == current_device) {
+    //   return;
+    // }
     ACL_CHECK(aclrtSetDevice(device));
 }
 
@@ -115,24 +116,6 @@ bool parse_bool(const std::string& value) {
     return valid_values.find(value) != valid_values.end();
 }
 
-/**
- * @brief Parse a string as an integer, returning 0 if invalid.
- *
- * This function attempts to convert the input string `value` to an `int`.
- * If the string is not a valid integer or is out of the `int` range,
- * it returns 0.
- *
- * @param value The string to parse.
- * @return The parsed integer, or 0 if conversion fails.
- */
-int parse_integer(const std::string& value) {
-    try {
-        return std::stoi(value);
-    } catch (...) {
-        return 0;
-    }
-}
-
 /**
  * @brief Initialize the CANN device information.
  *
@@ -1133,65 +1116,30 @@ static enum ggml_status ggml_backend_cann_buffer_init_tensor(
     return GGML_STATUS_SUCCESS;
 }
 
-/**
- * @brief Workspace for caching NZ buffers per device.
- *
- * This struct manages a device buffer used in NZ computations. It supports
- * allocation, reallocation, and clearing of cached memory. The struct is
- * designed to be used with a global array, one per device.
- */
-struct ggml_cann_nz_workspace {
-    void*  ptr;       // Pointer to allocated device buffer
-    size_t allocated; // Size of currently allocated buffer in bytes
+// ND to NZ Workspace Cache Management. Thread-safety: Not guaranteed
+namespace {
+    void* g_nz_workspace = nullptr;
+    size_t g_nz_workspace_allocated = 0;
 
-    /**
-     * @brief Constructor. Initializes the workspace with no allocated memory.
-     */
-    ggml_cann_nz_workspace() : ptr(nullptr), allocated(0) {}
-
-    /**
-     * @brief Free cached memory and reset the workspace.
-     *
-     * If a buffer has been allocated, this function releases it using
-     * aclrtFree and resets internal state.
-     */
-    void clear() {
-        if (ptr) {
-            ACL_CHECK(aclrtFree(ptr));
-            ptr = nullptr;
-            allocated = 0;
+    void release_nz_workspace() {
+        if (g_nz_workspace) {
+            aclrtFree(g_nz_workspace);
+            g_nz_workspace = nullptr;
+            g_nz_workspace_allocated = 0;
         }
     }
 
-    /**
-     * @brief Allocate or reallocate the workspace buffer.
-     *
-     * If the requested size is larger than the currently allocated size,
-     * the old buffer will be freed and a new buffer of the requested size
-     * will be allocated on the device.
-     *
-     * @param new_size Size in bytes to allocate for the workspace.
-     */
-    void realloc(size_t new_size) {
-        if (new_size > allocated) {
-            clear();
-            ACL_CHECK(aclrtMalloc(&ptr, new_size, ACL_MEM_MALLOC_HUGE_FIRST));
-            allocated = new_size;
+    void relloc_nz_workspace(size_t new_size) {
+        if (new_size > g_nz_workspace_allocated) {
+        if (g_nz_workspace) {
+            aclrtFree(g_nz_workspace);
+            g_nz_workspace = nullptr;
         }
+        ACL_CHECK(aclrtMalloc(&g_nz_workspace, new_size, ACL_MEM_MALLOC_HUGE_FIRST));
+        g_nz_workspace_allocated = new_size;
     }
-
-    /**
-     * @brief Get the device buffer pointer.
-     *
-     * @return Pointer to the allocated buffer, or nullptr if not allocated.
-     */
-    void* get() const { return ptr; }
-};
-
-/**
- * @brief Global array of NZ workspaces, one per device.
- */
-static ggml_cann_nz_workspace g_nz_workspaces[GGML_CANN_MAX_DEVICES];
+    }
+}
 
 /**
  * @brief Convert tensor weights to NZ format using Ascend CANN API.
@@ -1201,13 +1149,13 @@ static ggml_cann_nz_workspace g_nz_workspaces[GGML_CANN_MAX_DEVICES];
  * improve performance on certain hardware.
  *
  * @param tensor Pointer to the input ggml_tensor containing the weights.
+ * @param data Pointer to the raw data buffer for the tensor weights.
  * @param offset Byte offset within the tensor data buffer where weights start.
- * @param device device id.
  *
  * @note The workspace buffer used in this function is managed globally and reused
  *       across calls. This reduces overhead from repeated memory allocation and deallocation.
  */
-static void weight_format_to_nz(ggml_tensor *tensor, size_t offset, int device) {
+static void weight_format_to_nz(ggml_tensor *tensor, const void *data, size_t offset) {
     aclTensor* weightTransposed = ggml_cann_create_tensor(tensor, tensor->ne,
                                     tensor->nb, 2, ACL_FORMAT_ND, offset);
     uint64_t workspaceSize = 0;
@@ -1217,9 +1165,7 @@ static void weight_format_to_nz(ggml_tensor *tensor, size_t offset, int device)
     ACL_CHECK(aclnnTransMatmulWeightGetWorkspaceSize(weightTransposed,
                                                     &workspaceSize, &executor));
     // Avoid frequent malloc/free of the workspace.
-    g_nz_workspaces[device].realloc(workspaceSize);
-
-    void* g_nz_workspace = g_nz_workspaces[device].get();
+    relloc_nz_workspace(workspaceSize);
 
     ACL_CHECK(aclnnTransMatmulWeight(g_nz_workspace, workspaceSize, executor, nullptr));
     ACL_CHECK(aclDestroyTensor(weightTransposed));
@@ -1250,14 +1196,14 @@ static void ggml_backend_cann_buffer_set_tensor(
     // Why aclrtSynchronizeDevice?
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
     if (!need_transform(tensor->type)) {
         ACL_CHECK(aclrtMemcpy((char *)tensor->data + offset, size, data, size,
                               ACL_MEMCPY_HOST_TO_DEVICE));
         if (weight_to_nz && is_matmul_weight((const ggml_tensor*)tensor)) {
             GGML_ASSERT(tensor->ne[2] == 1);
             GGML_ASSERT(tensor->ne[3] == 1);
-            weight_format_to_nz(tensor, offset, ctx->device);
+            weight_format_to_nz(tensor, data, offset);
         }
     } else {
         void *transform_buffer = malloc(size);
@@ -1333,10 +1279,6 @@ static bool ggml_backend_cann_buffer_cpy_tensor(
                                   ACL_MEMCPY_DEVICE_TO_DEVICE));
             return true;
         } else {
-#ifdef ASCEND_310P
-            // TODO: Support 310p P2P copy
-            return false;
-#endif
             // Different device but can access by peer.
             int32_t canAccessPeer = 0;
             ACL_CHECK(aclrtDeviceCanAccessPeer(&canAccessPeer, src_ctx->device,
@@ -1497,7 +1439,7 @@ static size_t ggml_backend_cann_buffer_type_get_alloc_size(
     int64_t ne0 = tensor->ne[0];
 
     // Only check env once.
-    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or("on"));
+    static bool weight_to_nz = parse_bool(get_env("GGML_CANN_WEIGHT_NZ").value_or(""));
 
     // last line must bigger than 32, because every single op deal at
     // least 32 bytes.
@@ -2058,8 +2000,6 @@ static bool ggml_backend_cann_cpy_tensor_async(
     GGML_ASSERT(ggml_backend_is_cann(backend_src) ||
                 ggml_backend_is_cann(backend_dst));
 
-    GGML_ASSERT(!is_matmul_weight((const ggml_tensor*)src));
-
     if (!ggml_backend_buffer_is_cann(src->buffer) ||
         !ggml_backend_buffer_is_cann(dst->buffer)) {
         return false;
@@ -2080,10 +2020,6 @@ static bool ggml_backend_cann_cpy_tensor_async(
         return true;
     }
     if (backend_src != backend_dst) {
-#ifdef ASCEND_310P
-        // TODO: Support 310p P2P copy
-        return false;
-#endif
         ggml_backend_cann_buffer_context* buf_ctx_src =
             (ggml_backend_cann_buffer_context*)buf_src->context;
         ggml_backend_cann_buffer_context* buf_ctx_dst =
@@ -2100,6 +2036,7 @@ static bool ggml_backend_cann_cpy_tensor_async(
         }
 
         // need open both directions for memcpyasync between devices.
+        ggml_cann_set_device(cann_ctx_dst->device);
         ACL_CHECK(aclrtDeviceEnablePeerAccess(cann_ctx_src->device, 0));
         ggml_cann_set_device(cann_ctx_src->device);
         ACL_CHECK(aclrtDeviceEnablePeerAccess(cann_ctx_dst->device, 0));
@@ -2109,17 +2046,9 @@ static bool ggml_backend_cann_cpy_tensor_async(
         ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size,
                                    ACL_MEMCPY_DEVICE_TO_DEVICE,
                                    cann_ctx_src->stream()));
-        // record event on src stream after the copy
-        // TODO: this event is not effective with acl graph mode, change to use aclrtSynchronizeStream
-        // if (!cann_ctx_src->copy_event) {
-        //     ACL_CHECK(aclrtCreateEventWithFlag(&cann_ctx_src->copy_event, ACL_EVENT_SYNC));
-        // }
-        // ACL_CHECK(aclrtRecordEvent(cann_ctx_src->copy_event, cann_ctx_src->stream()));
 
-        // // wait on dst stream for the copy to complete
-        // ggml_cann_set_device(cann_ctx_dst->device);
-        // ACL_CHECK(aclrtStreamWaitEvent(cann_ctx_dst->stream(), cann_ctx_src->copy_event));
-        ACL_CHECK(aclrtSynchronizeStream(cann_ctx_src->stream()));
+        //TODO: workaround for Event didn`t work here.
+        aclrtSynchronizeStream(cann_ctx_src->stream());
     } else {
         // src and dst are on the same backend
         ACL_CHECK(aclrtMemcpyAsync(dst->data, copy_size, src->data, copy_size,
@@ -2148,52 +2077,30 @@ static void ggml_backend_cann_synchronize(ggml_backend_t backend) {
 
 #ifdef USE_ACL_GRAPH
 /**
- * @brief Add a new CANN graph to the LRU cache by populating node properties from the ggml graph.
+ * @brief Populate the internal CANN graph node properties from the ggml computation graph.
  *
- * This function creates a new ggml_cann_graph object and fills its node properties
- * (operation type, dimensions, strides, input sources, and operation parameters)
- * based on the current ggml computation graph.
+ * This function copies all node attributes (operation type, dimensions, strides, input sources,
+ * and operation parameters) into the cached CANN graph structure for later reuse or comparison.
  *
- * Each node in the ggml graph is mapped to a property entry in the new CANN graph:
- * - node address
- * - operation type
- * - shape (ne) and strides (nb)
- * - source tensor addresses
- * - operation parameters
- *
- * After initialization, the new graph is pushed into the LRU cache owned by the
- * CANN backend context. The cache takes ownership of the graph and manages its
- * lifetime (including deletion upon eviction).
- *
- * @param cann_ctx  The CANN backend context containing the graph cache.
- * @param cgraph    The current ggml computation graph.
+ * @param cann_ctx  The CANN backend context.
+ * @param cgraph    The ggml computational graph.
  */
-static void add_lru_matched_graph_node_properties(
-        ggml_backend_cann_context * cann_ctx,
-        ggml_cgraph * cgraph) {
-    // Create a new ggml_cann_graph object on the heap (its lifetime is managed by the cache).
-    ggml_cann_graph * new_graph = new ggml_cann_graph();
-    new_graph->ggml_graph_properties.resize(cgraph->n_nodes);
-
-    for (int node_idx = 0; node_idx < cgraph->n_nodes; ++node_idx) {
+static void set_ggml_graph_node_properties(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
+    for (int node_idx = 0; node_idx < cgraph->n_nodes; node_idx++) {
         ggml_tensor * node = cgraph->nodes[node_idx];
-        auto & prop = new_graph->ggml_graph_properties[node_idx];
+        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_address = node->data;
+        cann_ctx->cann_graph->ggml_graph_properties[node_idx].node_op = node->op;
 
-        prop.node_address = node->data;
-        prop.node_op      = node->op;
-
-        std::copy_n(node->ne, GGML_MAX_DIMS, prop.ne);
-        std::copy_n(node->nb, GGML_MAX_DIMS, prop.nb);
-
-        for (int src = 0; src < GGML_MAX_SRC; ++src) {
-            prop.src_address[src] = node->src[src] ? node->src[src]->data : nullptr;
+        for (int dim = 0; dim < GGML_MAX_DIMS; dim++) {
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].ne[dim] = node->ne[dim];
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].nb[dim] = node->nb[dim];
         }
-
-        memcpy(prop.op_params, node->op_params, GGML_MAX_OP_PARAMS);
+        for (int src = 0; src < GGML_MAX_SRC; src++) {
+            cann_ctx->cann_graph->ggml_graph_properties[node_idx].src_address[src] =
+                node->src[src] ? node->src[src]->data : nullptr;
+        }
+        memcpy(cann_ctx->cann_graph->ggml_graph_properties[node_idx].op_params, node->op_params, GGML_MAX_OP_PARAMS);
     }
-
-    // Insert into the LRU cache (cache takes ownership and will delete it when evicted).
-    cann_ctx->graph_lru_cache.push(new_graph);
 }
 
 /**
@@ -2238,45 +2145,30 @@ static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_gra
 }
 
 /**
- * @brief Check whether there is a cached CANN graph that matches the current ggml graph.
+ * @brief Determine if the CANN graph needs to be rebuilt due to graph changes.
  *
- * This function iterates through the cached CANN graphs stored in the LRU cache and
- * compares them against the given ggml computation graph. A match requires that the
- * number of nodes is the same and that each node’s properties (operation type,
- * dimensions, strides, inputs, and operation parameters) are identical.
+ * This checks whether the number or properties of ggml graph nodes have changed
+ * compared to the last captured CANN graph. If so, the CANN graph must be re-captured.
  *
- * If a matching graph is found, it is promoted to the front of the LRU cache and the
- * function returns true. Otherwise, the function returns false, indicating that a new
- * CANN graph needs to be captured.
- *
- * @param cann_ctx  The CANN backend context containing the graph cache.
+ * @param cann_ctx  The CANN backend context.
  * @param cgraph    The current ggml computation graph.
- * @return true if a matching cached graph exists; false otherwise.
+ * @return true if an update is required; false otherwise.
  */
-static bool is_matched_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
-    ggml_cann_graph_lru_cache &lru_cache = cann_ctx->graph_lru_cache;
-    for (auto &graph_ptr : lru_cache.cache_list) {
-        // Skip graphs with a different number of nodes.
-        if (graph_ptr->ggml_graph_properties.size() != static_cast<size_t>(cgraph->n_nodes)) {
-            continue;
-        }
+static bool is_cann_graph_update_required(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph) {
+    // The number of nodes is different, so the graph needs to be reconstructed.
+    if (cann_ctx->cann_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) {
+        cann_ctx->cann_graph->ggml_graph_properties.resize(cgraph->n_nodes);
+        return true;
+    }
 
-        // Check if all nodes match.
-        bool all_match = true;
-        for (int i = 0; i < cgraph->n_nodes; ++i) {
-            if (!ggml_graph_node_has_matching_properties(cgraph->nodes[i], &graph_ptr->ggml_graph_properties[i])) {
-                all_match = false;
-                break;
-            }
-        }
-
-        if (all_match) {
-            // update cache_list && renturn graph_ptr
-            lru_cache.move_to_front(graph_ptr);
+    // The number of nodes is the same; iterate over each node to check whether they match.
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        bool has_matching_properties = ggml_graph_node_has_matching_properties(
+            cgraph->nodes[i], &cann_ctx->cann_graph->ggml_graph_properties[i]);
+        if(!has_matching_properties) {
             return true;
         }
     }
-
     return false;
 }
 #endif  // USE_ACL_GRAPH
@@ -2295,13 +2187,17 @@ static bool is_matched_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph *
  * @param cann_graph_update_required Whether graph capture is needed due to graph changes.
  */
 static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx, ggml_cgraph * cgraph,
-    bool & use_cann_graph,  bool & cann_graph_update_required) {
+    bool & use_cann_graph, bool & cann_graph_update_required) {
 #ifdef USE_ACL_GRAPH
-    ggml_cann_graph* matched_graph = cann_ctx->graph_lru_cache.cache_list.front();
     if (use_cann_graph && cann_graph_update_required) {
+        if (cann_ctx->cann_graph->graph != nullptr) {
+            ACL_CHECK(aclmdlRIDestroy(cann_ctx->cann_graph->graph));
+            cann_ctx->cann_graph->graph = nullptr;
+        }
         ACL_CHECK(aclmdlRICaptureBegin(cann_ctx->stream(), ACL_MODEL_RI_CAPTURE_MODE_GLOBAL));
     }
 #endif // USE_ACL_GRAPH
+
     // Only perform the graph execution if CANN graphs are not enabled, or we are capturing the graph.
     // With the use of CANN graphs, the execution will be performed by the graph launch.
     if (!use_cann_graph || cann_graph_update_required) {
@@ -2322,12 +2218,12 @@ static void evaluate_and_capture_cann_graph(ggml_backend_cann_context * cann_ctx
 
 #ifdef USE_ACL_GRAPH
     if (use_cann_graph && cann_graph_update_required) { // End CANN graph capture
-        ACL_CHECK(aclmdlRICaptureEnd(cann_ctx->stream(), &matched_graph->graph));
+        ACL_CHECK(aclmdlRICaptureEnd(cann_ctx->stream(), &cann_ctx->cann_graph->graph));
     }
 
     if (use_cann_graph) {
         // Execute graph
-        ACL_CHECK(aclmdlRIExecuteAsync(matched_graph->graph, cann_ctx->stream()));
+        ACL_CHECK(aclmdlRIExecuteAsync(cann_ctx->cann_graph->graph, cann_ctx->stream()));
     }
 #endif // USE_ACL_GRAPH
 }
@@ -2350,46 +2246,25 @@ static enum ggml_status ggml_backend_cann_graph_compute(
     ggml_backend_cann_context* cann_ctx =
         (ggml_backend_cann_context*)backend->context;
     ggml_cann_set_device(cann_ctx->device);
-    g_nz_workspaces[cann_ctx->device].clear();
-
-    // calculate rope cache for fist layer in current device.
-    cann_ctx->rope_cache.cached = false;
-
+    release_nz_workspace();
 #ifdef USE_ACL_GRAPH
     bool use_cann_graph = true;
     bool cann_graph_update_required = false;
 
-    static bool prefill_use_graph = parse_bool(get_env("GGML_CANN_PREFILL_USE_GRAPH").value_or(""));
-    if (!prefill_use_graph) {
-        // Do not use acl_graph for prefill.
-        for (int i = 0; i < cgraph->n_nodes; i++) {
-            ggml_tensor * node = cgraph->nodes[i];
-            // TODO: Optimize here. Currently, we can only
-            // get seq_len by FA's input.
-            if (node->op == GGML_OP_FLASH_ATTN_EXT) {
-                // Q -> src[0], shape: [B, S, N, D]
-                use_cann_graph = (node->src[0]->ne[1] == 1);
-                break;
-            }
-        }
-    }
-
-    if (!cann_ctx->acl_graph_mode) {
-        use_cann_graph = false;
-    }
-
     if (use_cann_graph) {
-        // If no matching graph is found, the graph needs to be recaptured.
-        cann_graph_update_required = !is_matched_graph(cann_ctx, cgraph);
-        if (cann_graph_update_required) {
-            // If no matching graph is found, add a new ACL graph.
-            add_lru_matched_graph_node_properties(cann_ctx, cgraph);
+        if (cann_ctx->cann_graph == nullptr) {
+            cann_ctx->cann_graph.reset(new ggml_cann_graph());
+            cann_graph_update_required = true;
         }
+
+        cann_graph_update_required = is_cann_graph_update_required(cann_ctx, cgraph);
+        set_ggml_graph_node_properties(cann_ctx, cgraph);
     }
 #else
     bool use_cann_graph = false;
     bool cann_graph_update_required = false;
 #endif  // USE_ACL_GRAPH
+
     evaluate_and_capture_cann_graph(
         cann_ctx,
         cgraph,
@@ -2525,10 +2400,16 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
         }
         case GGML_OP_ROPE: {
             // TODO: with ops-test v == 1
+            float ext_factor = 0.0f;
+            memcpy(&ext_factor, (const float *) op->op_params + 7, sizeof(float));
             // TODO: n_dims <= ne0
             if (op->src[0]->ne[0] != op->op_params[1]) {
                 return false;
             }
+            // TODO: ext_factor != 0
+            if (ext_factor != 0) {
+                return false;
+            }
 
             const int mode = ((const int32_t *) op->op_params)[2];
             if (mode & GGML_ROPE_TYPE_MROPE) {
@@ -2537,11 +2418,10 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
             if (mode & GGML_ROPE_TYPE_VISION) {
                 return false;
             }
-#ifdef ASCEND_310P
+
             if(!ggml_is_contiguous(op->src[0])){
                 return false;
             }
-#endif
             return true;
         }
         case GGML_OP_UPSCALE: {
@@ -2603,17 +2483,15 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
         case GGML_OP_ARGMAX:
         case GGML_OP_COS:
         case GGML_OP_SIN:
+        case GGML_OP_CONV_TRANSPOSE_1D:
         case GGML_OP_LOG:
         case GGML_OP_MEAN:
         case GGML_OP_PAD_REFLECT_1D:
         case GGML_OP_COUNT_EQUAL:
             return true;
-        case GGML_OP_CONV_TRANSPOSE_1D:
-            // TODO: ((weightL - 1) * dilationW - padLeft)=1336 should not be larger than 255.
-            return (op->src[0]->ne[0] - 1) <= 255;
         case GGML_OP_SCALE:
             float bias;
-            memcpy(&bias, (const float *)(op->op_params) + 1, sizeof(float));
+            memcpy(&bias, (float*)op->op_params + 1, sizeof(float));
             return bias == 0.0f; // TODO: support bias != 0.0f
         case GGML_OP_SOFT_MAX:
             // TODO: support attention sinks [TAG_ATTN_SINKS]
@@ -2644,12 +2522,19 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
                 // different head sizes of K and V are not supported yet
                 return false;
             }
+            if (op->src[0]->ne[0] == 192) {
+                return false;
+            }
+            if (op->src[0]->ne[0] == 576) {
+                // DeepSeek MLA
+                return false;
+            }
             if (op->src[0]->ne[0] % 16 != 0) {
                 // TODO: padding to support
                 return false;
             }
             float logitSoftcap = 0.0f;
-            memcpy(&logitSoftcap, (const float *)(op->op_params) + 2, sizeof(float));
+            memcpy(&logitSoftcap,  (float*)op->op_params + 2, sizeof(float));
             if(logitSoftcap != 0.0f) {
                 return false;
             }
@@ -2756,7 +2641,6 @@ static const ggml_backend_i ggml_backend_cann_interface = {
     /* .graph_compute           = */ ggml_backend_cann_graph_compute,
     /* .event_record            = */ ggml_backend_cann_event_record,
     /* .event_wait              = */ ggml_backend_cann_event_wait,
-    /* .graph_optimize          = */ NULL,
 };
 
 /**

ggml/src/ggml-cpu/CMakeLists.txt

@@ -224,13 +224,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                 foreach(feature DOTPROD SVE MATMUL_INT8 FMA FP16_VECTOR_ARITHMETIC SME)
                     string(FIND "${ARM_FEATURE}" "__ARM_FEATURE_${feature} 1" feature_pos)
                     if (NOT ${feature_pos} EQUAL -1)
-                        # Special handling for MATMUL_INT8 when machine doesn't support i8mm
-                        if ("${feature}" STREQUAL "MATMUL_INT8" AND GGML_MACHINE_SUPPORTS_noi8mm)
-                            message(STATUS "ARM feature ${feature} detected but unsetting due to machine not supporting i8mm")
-                            list(APPEND ARCH_FLAGS -U__ARM_FEATURE_MATMUL_INT8)
-                        else()
-                            message(STATUS "ARM feature ${feature} enabled")
-                        endif()
+                        message(STATUS "ARM feature ${feature} enabled")
                     endif()
                 endforeach()
             endif()
@@ -439,22 +433,15 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             ggml-cpu/arch/riscv/quants.c
             ggml-cpu/arch/riscv/repack.cpp
             )
-        set(MARCH_STR "rv64gc")
-        if (GGML_RV_ZFH)
-            string(APPEND MARCH_STR "_zfh")
-        endif()
-        if (GGML_XTHEADVECTOR)
-            string(APPEND MARCH_STR "_xtheadvector")
-        elseif (GGML_RVV)
-            string(APPEND MARCH_STR "_v")
-            if (GGML_RV_ZVFH)
-                string(APPEND MARCH_STR "_zvfh")
+        if (GGML_RVV)
+            if (GGML_XTHEADVECTOR)
+                list(APPEND ARCH_FLAGS -march=rv64gc_zfhmin_xtheadvector -mabi=lp64d)
+            elseif (GGML_RV_ZFH)
+                list(APPEND ARCH_FLAGS -march=rv64gcv_zfhmin -mabi=lp64d)
+            else()
+                list(APPEND ARCH_FLAGS -march=rv64gcv -mabi=lp64d)
             endif()
         endif()
-        if (GGML_RV_ZICBOP)
-            string(APPEND MARCH_STR "_zicbop")
-        endif()
-        list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d)
     elseif (GGML_SYSTEM_ARCH STREQUAL "s390x")
         message(STATUS "s390x detected")
         list(APPEND GGML_CPU_SOURCES ggml-cpu/arch/s390/quants.c)
@@ -463,6 +450,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # TODO: Separation to determine activation of VX/VXE/VXE2
         if (${S390X_M} MATCHES "8561|8562")
+            set(GGML_NNPA OFF)
             message(STATUS "z15 target")
             list(APPEND ARCH_FLAGS -march=z15)
         elseif (${S390X_M} MATCHES "3931")
@@ -484,6 +472,11 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             list(APPEND ARCH_FLAGS -mvx -mzvector)
             list(APPEND ARCH_DEFINITIONS GGML_VXE)
         endif()
+
+        if (GGML_NNPA)
+            message(STATUS "NNPA enabled")
+            list(APPEND ARCH_DEFINITIONS GGML_NNPA)
+        endif()
     elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "wasm")
         message(STATUS "Wasm detected")
         list (APPEND GGML_CPU_SOURCES ggml-cpu/arch/wasm/quants.c)
@@ -504,9 +497,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         # Fetch KleidiAI sources:
         include(FetchContent)
-        set(KLEIDIAI_COMMIT_TAG "v1.13.0")
+        set(KLEIDIAI_COMMIT_TAG "v1.11.0")
         set(KLEIDIAI_DOWNLOAD_URL "https://github.com/ARM-software/kleidiai/archive/refs/tags/${KLEIDIAI_COMMIT_TAG}.tar.gz")
-        set(KLEIDIAI_ARCHIVE_MD5  "d82a8de939d9814621a5ba23907bdac1")
+        set(KLEIDIAI_ARCHIVE_MD5  "3fe9e5ab964c375c53839296eb71eaa2")
 
         if (POLICY CMP0135)
             cmake_policy(SET CMP0135 NEW)
@@ -562,7 +555,6 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
 
         list(APPEND GGML_KLEIDIAI_SOURCES
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32.c
-            ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p4x8sb_f32_neon.c
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32ps1s0scalef16_qsu4c32s16s0_neon.c
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_quant_pack_qsi8d32p_f32_neon.c
             ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_nxk_qsi4c32pscalef16_qsu4c32s16s0.c)
@@ -584,8 +576,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_f32_qsi8d32p_qsi4c32p/kai_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot.c
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/matmul_clamp_fp32_bf16p_bf16p/kai_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa.c
                 ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_lhs_pack_bf16p2vlx2_f32_sme.c
-                ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.c
-                ${KLEIDIAI_SRC}/kai/kai_common_sme_asm.S)
+                ${KLEIDIAI_SRC}/kai/ukernels/matmul/pack/kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.c)
             set(PRIVATE_ARCH_FLAGS "-fno-tree-vectorize;${PRIVATE_ARCH_FLAGS}+sve+sve2")
         endif()
 

ggml/src/ggml-cpu/amx/amx.cpp

@@ -7,7 +7,7 @@
 #include "ggml-cpu.h"
 #include "traits.h"
 
-#if defined(__linux__)
+#if defined(__gnu_linux__)
 #include <sys/syscall.h>
 #include <unistd.h>
 #endif
@@ -186,7 +186,7 @@ static size_t ggml_backend_amx_buffer_type_get_alloc_size(ggml_backend_buffer_ty
 #define XFEATURE_XTILEDATA      18
 
 static bool ggml_amx_init() {
-#if defined(__linux__)
+#if defined(__gnu_linux__)
     if (syscall(SYS_arch_prctl, ARCH_REQ_XCOMP_PERM, XFEATURE_XTILEDATA)) {
         fprintf(stderr, "AMX is not ready to be used!\n");
         return false;
@@ -194,8 +194,6 @@ static bool ggml_amx_init() {
     return true;
 #elif defined(_WIN32)
     return true;
-#else
-    return false;
 #endif
 }
 

ggml/src/ggml-cpu/arch/riscv/quants.c

@@ -1270,40 +1270,29 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             const float d = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].d);
             const float dmin = y[i].d * GGML_CPU_FP16_TO_FP32(x[i].dmin);
 
-            float ftmp, ft2;
-            const uint8_t * restrict q40;
-            const uint8_t * restrict q41;
-            const uint8_t * restrict q42;
-            const uint8_t * restrict q43;
-            const int8_t  * restrict q80;
-            const int8_t  * restrict q81;
-            const int8_t  * restrict q82;
-            const int8_t  * restrict q83;
-            int s0, s1, s2, s3;
-
+            int tmp, tmp2, sumi;
             __asm__ __volatile__(
-                "li %[s1], 8\n\t"
-                "vsetivli zero, 4, e32, m1, ta, ma\n\t"
-                "vle32.v v1, (%[s6b])\n\t"
-                "vslide1down.vx v1, v1, zero\n\t"
-                "vmv.v.x v16, zero\n\t"
+                "vsetivli zero, 12, e8, m1\n\t"
+                "vle8.v v1, (%[s6b])\n\t" // {aux[0], aux[1], aux[2]}
+                "vsetivli zero, 4, e32, m1\n\t"
                 "vslidedown.vi v2, v1, 2\n\t"
                 "vmv1r.v v3, v2\n\t"
                 "vslideup.vi v2, v3, 1\n\t" // {aux[2], aux[2]}
-                "vsetivli zero, 2, e32, m1, ta, ma\n\t"
+                "vsetivli zero, 2, e32, m1\n\t"
                 "vmv.v.i v4, 4\n\t"
                 "vand.vx v8, v1, %[kmask1]\n\t"
                 "vslide1up.vx v5, v4, zero\n\t" // {0, 4}
                 "vsrl.vi v6, v1, 6\n\t"
                 "vsrl.vv v7, v2, v5\n\t"
-                "vsse32.v v8, (%[utmp]), %[s1]\n\t"
                 "vand.vx v0, v6, %[kmask3]\n\t"
                 "vand.vx v2, v7, %[kmask2]\n\t"
                 "vsll.vi v6, v0, 4\n\t"
-                "addi %[s0], %[utmp], 4\n\t"
+                "li %[t2], 8\n\t"
+                "addi %[t1], %[utmp], 4\n\t"
                 "vor.vv v1, v6, v2\n\t"
-                "vsse32.v v1, (%[s0]), %[s1]\n\t"
-                "vsetivli zero, 8, e16, m1, ta, ma\n\t"
+                "vsse32.v v8, (%[utmp]), %[t2]\n\t"
+                "vsse32.v v1, (%[t1]), %[t2]\n\t"
+                "vsetivli zero, 8, e16, m1\n\t"
                 "vle32.v v2, (%[bsums])\n\t"
                 "vnsrl.wi v0, v2, 0\n\t"
                 "vnsrl.wi v1, v2, 16\n\t"
@@ -1311,131 +1300,13 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
                 "vle8.v v3, (%[mins])\n\t"
                 "vzext.vf2 v4, v3\n\t"
                 "vwmul.vv v6, v4, v2\n\t"
-                "vsetivli zero, 4, e32, m1, ta, ma\n\t"
-                "vredsum.vs v0, v6, v16\n\t"
-                "vredsum.vs v0, v7, v0\n\t"
-                "vfcvt.f.x.v v0, v0\n\t"
-                "vfmv.f.s %[ftmp], v0\n\t"
-                "vsetivli zero, 16, e8, m1, ta, ma\n\t"
-                "vle8.v v0, (%[xs])\n\t"
-                "fnmsub.s %[sumf], %[dmin], %[ftmp], %[sumf]\n\t"
-                "addi %[q40], %[xs], 64\n\t"
-                "addi %[q41], %[xs], 16\n\t"
-                "addi %[q42], %[xs], 32\n\t"
-                "addi %[q43], %[xs], 48\n\t"
-                "addi %[q80], %[ys], 64\n\t"
-                "vle8.v v1, (%[q41])\n\t"
-                "vle8.v v2, (%[q42])\n\t"
-                "addi %[q81], %[ys], 16\n\t"
-                "addi %[q41], %[q41], 64\n\t"
-                "addi %[q82], %[ys], 32\n\t"
-                "vle8.v v3, (%[q43])\n\t"
-                "vle8.v v8, (%[ys])\n\t"
-                "addi %[q42], %[q42], 64\n\t"
-                "addi %[q83], %[ys], 48\n\t"
-                "addi %[q43], %[q43], 64\n\t"
-                "vsrl.vi v4, v0, 4\n\t"
-                "vle8.v v9, (%[q81])\n\t"
-                "vle8.v v10, (%[q82])\n\t"
-                "vand.vi v0, v0, 0xF\n\t"
-                "addi %[q81], %[q81], 64\n\t"
-                "vsrl.vi v5, v1, 4\n\t"
-                "addi %[q82], %[q82], 64\n\t"
-                "vle8.v v11, (%[q83])\n\t"
-                "vle8.v v12, (%[q80])\n\t"
-                "vand.vi v1, v1, 0xF\n\t"
-                "addi %[q83], %[q83], 64\n\t"
-                "vsrl.vi v6, v2, 4\n\t"
-                "addi %[q80], %[q80], 64\n\t"
-                "vle8.v v13, (%[q81])\n\t"
-                "vle8.v v14, (%[q82])\n\t"
-                "vand.vi v2, v2, 0xF\n\t"
-                "addi %[q81], %[q81], 64\n\t"
-                "vsrl.vi v7, v3, 4\n\t"
-                "addi %[q82], %[q82], 64\n\t"
-                "vwmul.vv v16, v0, v8\n\t"
-                "vle8.v v15, (%[q83])\n\t"
-                "vle8.v v0, (%[q40])\n\t"
-                "vand.vi v3, v3, 0xF\n\t"
-                "addi %[q83], %[q83], 64\n\t"
-                "vwmul.vv v24, v2, v12\n\t"
-                "vwmul.vv v20, v4, v10\n\t"
-                "vwmul.vv v28, v6, v14\n\t"
-                "vwmacc.vv v16, v1, v9\n\t"
-                "vle8.v v1, (%[q41])\n\t"
-                "vle8.v v2, (%[q42])\n\t"
-                "vwmacc.vv v24, v3, v13\n\t"
-                "vwmacc.vv v20, v5, v11\n\t"
-                "vwmacc.vv v28, v7, v15\n\t"
-                "addi %[q40], %[q80], 64\n\t"
-                "addi %[q41], %[q81], 64\n\t"
-                "vle8.v v3, (%[q43])\n\t"
-                "vle8.v v8, (%[q80])\n\t"
-                "addi %[q42], %[q82], 64\n\t"
-                "addi %[q43], %[q83], 64\n\t"
-                "vsrl.vi v4, v0, 4\n\t"
-                "vle8.v v9, (%[q81])\n\t"
-                "vle8.v v10, (%[q82])\n\t"
-                "vand.vi v0, v0, 0xF\n\t"
-                "vsrl.vi v5, v1, 4\n\t"
-                "vsrl.vi v7, v3, 4\n\t"
-                "vand.vi v3, v3, 0xF\n\t"
-                "vle8.v v11, (%[q83])\n\t"
-                "vle8.v v12, (%[q40])\n\t"
-                "vand.vi v1, v1, 0xF\n\t"
-                "vsrl.vi v6, v2, 4\n\t"
-                "vand.vi v2, v2, 0xF\n\t"
-                "vwmul.vv v18, v0, v8\n\t"
-                "vle8.v v13, (%[q41])\n\t"
-                "vle8.v v14, (%[q42])\n\t"
-                "vwmul.vv v26, v2, v12\n\t"
-                "vwmul.vv v22, v4, v10\n\t"
-                "vwmul.vv v30, v6, v14\n\t"
-                "vwmacc.vv v18, v1, v9\n\t"
-                "vle8.v v15, (%[q43])\n\t"
-                "vwmacc.vv v26, v3, v13\n\t"
-                "vwmacc.vv v22, v5, v11\n\t"
-                "vwmacc.vv v30, v7, v15\n\t"
                 "vmv.v.x v0, zero\n\t"
-                "vsetivli zero, 16, e16, m2, ta, ma\n\t"
-                "vwredsum.vs v4, v16, v0\n\t"
-                "lbu %[s0], 0(%[scale])\n\t"
-                "vwredsum.vs v5, v20, v0\n\t"
-                "lbu %[s1], 1(%[scale])\n\t"
-                "vwredsum.vs v6, v24, v0\n\t"
-                "lbu %[s2], 2(%[scale])\n\t"
-                "vwredsum.vs v7, v28, v0\n\t"
-                "lbu %[s3], 3(%[scale])\n\t"
-                "vwredsum.vs v8, v18, v0\n\t"
-                "lbu %[q40], 4(%[scale])\n\t"
-                "vwredsum.vs v9, v22, v0\n\t"
-                "lbu %[q41], 5(%[scale])\n\t"
-                "vwredsum.vs v10, v26, v0\n\t"
-                "lbu %[q42], 6(%[scale])\n\t"
-                "vwredsum.vs v11, v30, v0\n\t"
-                "lbu %[q43], 7(%[scale])\n\t"
-                "vsetivli zero, 4, e32, m1, ta, ma\n\t"
-                "vmul.vx v0, v4, %[s0]\n\t"
-                "vmul.vx v1, v8, %[q40]\n\t"
-                "vmacc.vx v0, %[s1], v5\n\t"
-                "vmacc.vx v1, %[q41], v9\n\t"
-                "vmacc.vx v0, %[s2], v6\n\t"
-                "vmacc.vx v1, %[q42], v10\n\t"
-                "vmacc.vx v0, %[s3], v7\n\t"
-                "vmacc.vx v1, %[q43], v11\n\t"
-                "vfcvt.f.x.v v0, v0\n\t"
-                "vfcvt.f.x.v v1, v1\n\t"
-                "vfmv.f.s %[ft2], v0\n\t"
-                "vfmv.f.s %[ftmp], v1\n\t"
-                "fadd.s %[ft2], %[ft2], %[ftmp]\n\t"
-                "fmadd.s %[sumf], %[d], %[ft2], %[sumf]"
-                : [ftmp] "=&f" (ftmp), [sumf] "+&f" (sumf), [ft2] "=&f" (ft2)
-                , [s0] "=&r" (s0), [s1] "=&r" (s1), [s2] "=&r" (s2), [s3] "=&r" (s3)
-                , [q40] "=&r" (q40), [q41] "=&r" (q41), [q42] "=&r" (q42), [q43] "=&r" (q43)
-                , [q80] "=&r" (q80), [q81] "=&r" (q81), [q82] "=&r" (q82), [q83] "=&r" (q83)
-                : [d] "f" (d), [ys] "r" (y[i].qs), [xs] "r" (x[i].qs), [scale] "r" (scales)
-                , [bsums] "r" (y[i].bsums), [mins] "r" (mins), [utmp] "r" (utmp)
-                , [s6b] "r" (&x[i]), [kmask1] "r" (kmask1), [dmin] "f" (dmin)
+                "vsetivli zero, 8, e32, m2\n\t"
+                "vredsum.vs v0, v6, v0\n\t"
+                "vmv.x.s %[sumi], v0"
+                : [t1] "=&r" (tmp), [t2] "=&r" (tmp2), [sumi] "=&r" (sumi)
+                : [bsums] "r" (y[i].bsums), [mins] "r" (mins), [utmp] "r" (utmp)
+                , [s6b] "r" (x[i].scales), [kmask1] "r" (kmask1)
                 , [kmask2] "r" (kmask2), [kmask3] "r" (kmask3)
                 : "memory"
                 , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
@@ -1443,6 +1314,59 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
                 , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
                 , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
             );
+            sumf -= dmin * sumi;
+
+            const uint8_t * restrict q4 = x[i].qs;
+            const int8_t  * restrict q8 = y[i].qs;
+
+            sumi = 0;
+            const uint8_t * scale = scales;
+
+            for (int j = 0; j < QK_K/128; ++j) {
+                int vl128 = 128, vl64 = 64, vl32 = 32;
+                __asm__ __volatile__(
+                    "vsetvli zero, %[vl128], e8, m8\n\t"
+                    "vle8.v v8, (%[q8])\n\t"
+                    "vsetvli zero, %[vl64], e8, m4\n\t"
+                    "vle8.v v0, (%[q4])\n\t"
+                    "vsrl.vi v4, v0, 4\n\t"
+                    "vand.vi v0, v0, 0xF\n\t"
+                    "vsetvli zero, %[vl32], e8, m2\n\t"
+                    "vwmul.vv v28, v6, v14\n\t"
+                    "vwmul.vv v20, v4, v10\n\t"
+                    "vwmul.vv v24, v2, v12\n\t"
+                    "vwmul.vv v16, v0, v8\n\t"
+                    "vsetivli zero, 4, e32, m1\n\t"
+                    "vle8.v v2, (%[scale])\n\t"
+                    "vmv.v.x v0, zero\n\t"
+                    "vzext.vf4 v1, v2\n\t"
+                    "vsetvli zero, %[vl32], e16, m4\n\t"
+                    "vwredsum.vs v6, v24, v0\n\t"
+                    "vwredsum.vs v7, v28, v0\n\t"
+                    "vwredsum.vs v4, v16, v0\n\t"
+                    "vwredsum.vs v5, v20, v0\n\t"
+                    "vsetivli zero, 4, e32, m1\n\t"
+                    "vslideup.vi v6, v7, 1\n\t"
+                    "vslideup.vi v4, v5, 1\n\t"
+                    "vslideup.vi v4, v6, 2\n\t"
+                    "vmul.vv v8, v4, v1\n\t"
+                    "vredsum.vs v0, v8, v0\n\t"
+                    "vmv.x.s %[tmp], v0\n\t"
+                    "add %[sumi], %[sumi], %[tmp]"
+                    : [tmp] "=&r" (tmp), [sumi] "+&r" (sumi)
+                    : [vl128] "r" (vl128), [vl64] "r" (vl64), [vl32] "r" (vl32)
+                    , [q4] "r" (q4), [q8] "r" (q8), [scale] "r" (scale)
+                    : "memory"
+                    , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
+                    , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
+                    , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
+                    , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
+                );
+
+                q4 += 64;    q8 += 128;    scale += 4;
+            }
+
+            sumf += d * sumi;
         }
         break;
     default:
@@ -1769,8 +1693,6 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
     case 128:
         for (int i = 0; i < nb; ++i) {
 
-            __builtin_prefetch(&x[i + 1].d, 0, 1);
-
             const float d = GGML_CPU_FP16_TO_FP32(x[i].d) * y[i].d;
 
             const uint8_t * restrict q6 = x[i].ql;
@@ -1779,59 +1701,23 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 
             const int8_t * restrict scale = x[i].scales;
 
-            int q6h;
-            float ftmp;
+            int sum_t = 0;
+            int t0;
 
             for (int j = 0; j < QK_K/128; ++j) {
                 __asm__ __volatile__(
-                    "addi %[q6h], %[q6], 32\n\t"
-                    "ld t0, 0(%[scale])\n\t"
-                    "addi %[scale], %[scale], 8\n\t"
-                    "slli t6, t0, 1 * 8\n\t"
-                    "lb zero, 0(%[q6])\n\t"
-                    "slli t5, t0, 2 * 8\n\t"
-                    "slli t4, t0, 3 * 8\n\t"
-                    "lb zero, 0(%[q6h])\n\t"
-                    "slli t3, t0, 4 * 8\n\t"
-                    "slli t2, t0, 5 * 8\n\t"
-                    "lb zero, 0(%[qh])\n\t"
-                    "lb zero, 31(%[q6h])\n\t"
-                    "slli t1, t0, 6 * 8\n\t"
-                    "srai a7, t0, 56\n\t"
                     "vsetvli zero, %[vl32], e8, m2\n\t"
-                    "vle8.v v8, (%[q6])\n\t"
-                    "srai t6, t6, 56\n\t"
-                    "srai t5, t5, 56\n\t"
-                    "srai t4, t4, 56\n\t"
-                    "srai t3, t3, 56\n\t"
-                    "vle8.v v10, (%[q6h])\n\t"
-                    "addi %[q6], %[q6], 64\n\t"
-                    "slli t0, t0, 7 * 8\n\t"
-                    "srai t2, t2, 56\n\t"
-                    "srai t1, t1, 56\n\t"
-                    "srai t0, t0, 56\n\t"
                     "vle8.v v4, (%[qh])\n\t"
-                    "vsrl.vi v12, v8, 4\n\t"
-                    "vsrl.vi v14, v10, 4\n\t"
-                    "lb zero, 0(%[q8])\n\t"
-                    "vand.vi v8, v8, 0xF\n\t"
-                    "vand.vi v10, v10, 0xF\n\t"
-                    "lb zero, 32(%[q8])\n\t"
                     "vsll.vi v0, v4, 4\n\t"
                     "vsll.vi v2, v4, 2\n\t"
-                    "lb zero, 64(%[q8])\n\t"
                     "vsrl.vi v6, v4, 2\n\t"
-                    "vand.vx v0, v0, %[mask]\n\t"
-                    "lb zero, 96(%[q8])\n\t"
-                    "vand.vx v2, v2, %[mask]\n\t"
-                    "vand.vx v4, v4, %[mask]\n\t"
-                    "vand.vx v6, v6, %[mask]\n\t"
-                    "vor.vv v8, v8, v0\n\t"
-                    "lb zero, 127(%[q8])\n\t"
-                    "vor.vv v10, v10, v2\n\t"
-                    "vor.vv v12, v12, v4\n\t"
-                    "vor.vv v14, v14, v6\n\t"
+                    "vsetvli zero, %[vl64], e8, m4\n\t"
+                    "vle8.v v8, (%[q6])\n\t"
+                    "vsrl.vi v12, v8, 4\n\t"
+                    "vand.vi v8, v8, 0xF\n\t"
                     "vsetvli zero, %[vl128], e8, m8\n\t"
+                    "vand.vx v0, v0, %[mask]\n\t"
+                    "vor.vv v8, v8, v0\n\t"
                     "vle8.v v0, (%[q8])\n\t"
                     "vsub.vx v8, v8, %[vl32]\n\t"
                     "vsetvli zero, %[vl64], e8, m4\n\t"
@@ -1848,34 +1734,34 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
                     "vwredsum.vs v13, v28, v0\n\t"
                     "vwredsum.vs v14, v30, v0\n\t"
                     "vsetivli zero, 4, e32, m1\n\t"
-                    "vmul.vx v0, v10, t0\n\t"
-                    "vmul.vx v1, v9, t1\n\t"
-                    "vmacc.vx v0, t2, v8\n\t"
-                    "vmacc.vx v1, t3, v7\n\t"
-                    "vmacc.vx v0, t4, v11\n\t"
-                    "vmacc.vx v1, t5, v12\n\t"
-                    "vmacc.vx v0, t6, v13\n\t"
-                    "vmacc.vx v1, a7, v14\n\t"
-                    "vadd.vv v0, v0, v1\n\t"
-                    "vfcvt.f.x.v v0, v0\n\t"
-                    "vfmv.f.s %[ftmp], v0\n\t"
-                    "fmadd.s %[sumf], %[d], %[ftmp], %[sumf]"
-                    : [q6] "+&r" (q6), [q6h] "=&r" (q6h)
-                    , [scale] "+&r" (scale)
-                    , [sumf] "+&f" (sumf), [ftmp] "=&f" (ftmp)
-                    : [qh] "r" (qh), [q8] "r" (q8)
+                    "vslideup.vi v10, v9, 1\n\t"
+                    "vslideup.vi v8, v7, 1\n\t"
+                    "vslideup.vi v11, v12, 1\n\t"
+                    "vslideup.vi v13, v14, 1\n\t"
+                    "vslideup.vi v10, v8, 2\n\t"
+                    "vslideup.vi v11, v13, 2\n\t"
+                    "vsetivli zero, 8, e32, m2\n\t"
+                    "vle8.v v2, (%[scale])\n\t"
+                    "vsext.vf4 v4, v2\n\t"
+                    "vmul.vv v2, v4, v10\n\t"
+                    "vredsum.vs v0, v2, v0\n\t"
+                    "vmv.x.s %[t0], v0\n\t"
+                    "add %[sumi], %[sumi], %[t0]"
+                    : [sumi] "+&r" (sum_t), [t0] "=&r" (t0)
+                    : [qh] "r" (qh), [q6] "r" (q6), [q8] "r" (q8), [scale] "r" (scale)
                     , [vl32] "r" (32), [vl64] "r" (64), [vl128] "r" (128)
-                    , [mask] "r" (0x30), [d] "f" (d)
+                    , [mask] "r" (0x30)
                     : "memory"
                     , "v0", "v1", "v2", "v3", "v4", "v5", "v6", "v7"
                     , "v8", "v9", "v10", "v11", "v12", "v13", "v14", "v15"
                     , "v16", "v17", "v18", "v19", "v20", "v21", "v22", "v23"
                     , "v24", "v25", "v26", "v27", "v28", "v29", "v30", "v31"
-                    , "t0", "t1", "t2", "t3", "t4", "t5", "t6", "a7"
-                    , "a6", "a5", "a4", "a3"
                 );
-                qh += 32;   q8 += 128;
+                q6 += 64;   qh += 32;   q8 += 128;   scale += 8;
             }
+
+            sumf += d * sum_t;
+
         }
         break;
     default:

ggml/src/ggml-cpu/arch/s390/quants.c

@@ -53,9 +53,9 @@ void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
 
 #if defined(__VXE__) || defined(__VXE2__)
     for (int i = 0; i < nb; i++) {
-        float32x4_t srcv [8];
-        float32x4_t asrcv[8];
-        float32x4_t amaxv[8];
+        __vector float srcv [8];
+        __vector float asrcv[8];
+        __vector float amaxv[8];
 
         for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j);
         for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]);
@@ -74,8 +74,8 @@ void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
         y[i].d = GGML_CPU_FP32_TO_FP16(d);
 
         for (int j = 0; j < 8; j++) {
-            const float32x4_t v = vec_mul(srcv[j], vec_splats(id));
-            const int32x4_t vi = vec_signed(v);
+            const __vector float v = vec_mul(srcv[j], vec_splats(id));
+            const __vector int32_t vi = vec_signed(v);
 
             y[i].qs[4*j + 0] = vec_extract(vi, 0);
             y[i].qs[4*j + 1] = vec_extract(vi, 1);
@@ -98,9 +98,9 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
 
 #if defined(__VXE__) || defined(__VXE2__)
     for (int i = 0; i < nb; i++) {
-        float32x4_t srcv [8];
-        float32x4_t asrcv[8];
-        float32x4_t amaxv[8];
+        __vector float srcv [8];
+        __vector float asrcv[8];
+        __vector float amaxv[8];
 
         for (int j = 0; j < 8; j++) srcv[j] = vec_xl(0, x + i*32 + 4*j);
         for (int j = 0; j < 8; j++) asrcv[j] = vec_abs(srcv[j]);
@@ -118,11 +118,11 @@ void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, i
 
         y[i].d = GGML_CPU_FP32_TO_FP16(d);
 
-        int32x4_t acc = vec_splats(0);
+        __vector int32_t acc = vec_splats(0);
 
         for (int j = 0; j < 8; j++) {
-            const float32x4_t v = vec_mul(srcv[j], vec_splats(id));
-            const int32x4_t vi = vec_signed(v);
+            const __vector float v = vec_mul(srcv[j], vec_splats(id));
+            const __vector int32_t vi = vec_signed(v);
 
             y[i].qs[4*j + 0] = vec_extract(vi, 0);
             y[i].qs[4*j + 1] = vec_extract(vi, 1);
@@ -162,36 +162,37 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     float sumf = 0;
 
 #if defined(__VXE__) || defined(__VXE2__)
-    float32x4_t acc = vec_splats(0.0f);
+    __vector float acc = vec_splats(0.0f);
 
-    const uint8x16_t v_m = vec_splats((const uint8_t)0x0F);
-    const int8x16_t  v_s = vec_splats( (const int8_t)0x08);
+    const __vector uint8_t v_m = vec_splats((const uint8_t)0x0F);
+    const __vector int8_t  v_s = vec_splats( (const int8_t)0x08);
 
     for (; ib < nb; ++ib) {
-        const uint8x16_t v_x = vec_xl(0, x[ib].qs);
-        const int8x16_t v_xl = (const int8x16_t)(v_x & v_m);
-        const int8x16_t v_xh = (const int8x16_t)(v_x >> 4);
+        const __vector uint8_t v_x = vec_xl(0, x[ib].qs);
+        const __vector int8_t v_xl = (const __vector int8_t)(v_x & v_m);
+        const __vector int8_t v_xh = (const __vector int8_t)(v_x >> 4);
 
-        const int8x16_t v_xls = vec_sub(v_xl, v_s);
-        const int8x16_t v_xhs = vec_sub(v_xh, v_s);
+        const __vector int8_t v_xls = vec_sub(v_xl, v_s);
+        const __vector int8_t v_xhs = vec_sub(v_xh, v_s);
 
-        const int8x16_t v_yl = vec_xl(0      , y[ib].qs);
-        const int8x16_t v_yh = vec_xl(QK8_0/2, y[ib].qs);
+        const __vector int8_t v_yl = vec_xl(0      , y[ib].qs);
+        const __vector int8_t v_yh = vec_xl(QK8_0/2, y[ib].qs);
 
-        const int16x8_t v_xylso = vec_mulo(v_xls, v_yl);
-        const int16x8_t v_xylse = vec_mule(v_xls, v_yl);
-        const int16x8_t v_xyhso = vec_mulo(v_xhs, v_yh);
-        const int16x8_t v_xyhse = vec_mule(v_xhs, v_yh);
+        const __vector int16_t v_xylso = vec_mulo(v_xls, v_yl);
+        const __vector int16_t v_xylse = vec_mule(v_xls, v_yl);
+        const __vector int16_t v_xyhso = vec_mulo(v_xhs, v_yh);
+        const __vector int16_t v_xyhse = vec_mule(v_xhs, v_yh);
 
-        int16x8_t v_xy_ = v_xylso + v_xylse + v_xyhso + v_xyhse; v_xy_ += vec_reve(v_xy_);
+        __vector int16_t v_xy_ = v_xylso + v_xylse + v_xyhso + v_xyhse; v_xy_ += vec_reve(v_xy_);
 
-        const float32x4_t v_xy = vec_float(vec_unpackh(v_xy_));
-        const float32x4_t v_d = vec_splats(GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d));
+        const __vector float v_xy = vec_float(vec_unpackh(v_xy_));
+        const __vector float v_d = vec_splats(GGML_CPU_FP16_TO_FP32(x[ib].d) * GGML_CPU_FP16_TO_FP32(y[ib].d));
 
         acc = vec_madd(v_xy, v_d, acc);
     }
 
-    sumf = vec_hsum_f32x4(acc);
+    sumf = acc[0] + acc[1] + acc[2] + acc[3];
+
     *s = sumf;
 #else
     UNUSED(nb);
@@ -248,7 +249,8 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
         acc = vec_madd(v_xy, v_d, acc);
     }
 
-    sumf = vec_hsum_f32x4(acc) + summs;
+    sumf = acc[0] + acc[1] + acc[2] + acc[3] + summs;
+
     *s = sumf;
 #else
     UNUSED(nb);
@@ -349,7 +351,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
         v_sum1 = vec_madd(v_xy1f, v_d1, v_sum1);
     }
 
-    sumf += vec_hsum_f32x4(v_sum0) + vec_hsum_f32x4(v_sum1);
+    sumf += vec_hsum(v_sum0) + vec_hsum(v_sum1);
 
     #pragma GCC unroll 4
     for (; ib < nb; ++ib) {
@@ -388,7 +390,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
         const float32x4_t v_d = vec_splats(GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d));
         const float32x4_t v_acc = vec_madd(v_xyf, v_d, vec_splats(0.0f));
 
-        sumf += vec_hsum_f32x4(v_acc);
+        sumf += vec_hsum(v_acc);
     }
 
     *s = sumf;
@@ -500,7 +502,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
         v_sum1 = vec_madd(v_xy1f, v_d1, v_sum1);
     }
 
-    sumf += vec_hsum_f32x4(v_sum0) + vec_hsum_f32x4(v_sum1) + summs0 + summs1;
+    sumf += vec_hsum(v_sum0) + vec_hsum(v_sum1) + summs0 + summs1;
 
     #pragma GCC unroll 4
     for (; ib < nb; ++ib) {
@@ -541,7 +543,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const voi
         const float32x4_t v_d = vec_splats(GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d));
         const float32x4_t v_acc = vec_madd(v_xyf, v_d, v_acc);
 
-        sumf += vec_hsum_f32x4(v_acc) + summs;
+        sumf += vec_hsum(v_acc) + summs;
     }
 
     *s = sumf;
@@ -573,7 +575,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
     float sumf = 0;
 
 #if defined(__VXE__) || defined(__VXE2__)
-    float32x4_t acc = vec_splats(0.0f);
+    __vector float acc = vec_splats(0.0f);
 
 #pragma GCC unroll 8
     for (; ib < nb; ++ib) {
@@ -592,7 +594,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
         acc = vec_madd(v_xy, v_d, acc);
     }
 
-    sumf = vec_hsum_f32x4(acc);
+    sumf = acc[0] + acc[1] + acc[2] + acc[3];
 
     *s = sumf;
 #else
@@ -716,10 +718,10 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             isum2 = ggml_vec_dot(v_z, q3bytes[2], q8bytes[6]);
             isum3 = ggml_vec_dot(v_z, q3bytes[3], q8bytes[7]);
 
-            isum += vec_hsum_i32x4(isum0) * scale[0];
-            isum += vec_hsum_i32x4(isum1) * scale[1];
-            isum += vec_hsum_i32x4(isum2) * scale[2];
-            isum += vec_hsum_i32x4(isum3) * scale[3];
+            isum += (isum0[0] + isum0[1] + isum0[2] + isum0[3]) * scale[0];
+            isum += (isum1[0] + isum1[1] + isum1[2] + isum1[3]) * scale[1];
+            isum += (isum2[0] + isum2[1] + isum2[2] + isum2[3]) * scale[2];
+            isum += (isum3[0] + isum3[1] + isum3[2] + isum3[3]) * scale[3];
 
             scale += 4;
 
@@ -817,7 +819,7 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             v_xl[1] = (int8x16_t)vec_and(v_x[1], v_lm);
 
             const int32x4_t p1 = ggml_vec_dot(ggml_vec_dot(v_z, v_xl[0], v_y[0]), v_xl[1], v_y[1]);
-            sumi1 += vec_hsum_i32x4(p1) * scales[2*j+0];
+            sumi1 += (p1[0] + p1[1] + p1[2] + p1[3]) * scales[2*j+0];
 
             v_y[0] = vec_xl(0 , y0);
             v_y[1] = vec_xl(16, y0);
@@ -827,7 +829,7 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             v_xl[1] = (int8x16_t)vec_sr(v_x[1], 4);
 
             const int32x4_t p2 = ggml_vec_dot(ggml_vec_dot(v_z, v_xl[0], v_y[0]), v_xl[1], v_y[1]);
-            sumi2 += vec_hsum_i32x4(p2) * scales[2*j+1];
+            sumi2 += (p2[0] + p2[1] + p2[2] + p2[3]) * scales[2*j+1];
         }
 
         sumf += d * (sumi1 + sumi2);
@@ -909,7 +911,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
         const int32x4_t v_minsho = vec_mulo(v_ysums, v_minsh);
         const int32x4_t v_minshe = vec_mule(v_ysums, v_minsh);
         const int32x4_t v_mins = vec_add(v_minsho, v_minshe);
-        const int32_t mins = vec_hsum_i32x4(v_mins);
+        const int32_t mins = v_mins[0] + v_mins[1] + v_mins[2] + v_mins[3];
 
         const uint8_t * scales = (const uint8_t *)utmp;
         const uint8_t * GGML_RESTRICT x0l = x[i].qs;
@@ -946,8 +948,8 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             int32x4_t sumi0 = ggml_vec_dot(ggml_vec_dot(v_z, q5b[0], v_y[0]), q5b[1], v_y[1]);
             int32x4_t sumi1 = ggml_vec_dot(ggml_vec_dot(v_z, q5b[2], v_y[2]), q5b[3], v_y[3]);
 
-            sumi += vec_hsum_i32x4(sumi0) * *scales++;
-            sumi += vec_hsum_i32x4(sumi1) * *scales++;
+            sumi += (sumi0[0] + sumi0[1] + sumi0[2] + sumi0[3]) * *scales++;
+            sumi += (sumi1[0] + sumi1[1] + sumi1[2] + sumi1[3]) * *scales++;
         }
 
         sumf += d * sumi - dmin * mins;
@@ -1018,7 +1020,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
         const int32x4_t v_minshe = vec_mule(v_ysumsh, v_scaleh);
         const int32x4_t v_mins = v_minslo + v_minsle + v_minsho + v_minshe;
 
-        const int32_t mins = vec_hsum_i32x4(v_mins);
+        const int32_t mins = v_mins[0] + v_mins[1] + v_mins[2] + v_mins[3];
 
         int32_t isum = 0;
         for (int j = 0; j < QK_K/128; ++j) {
@@ -1058,10 +1060,10 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             int32x4_t summs2 = ggml_vec_dot(v_z, q6b[2], v_y[2]);
             int32x4_t summs3 = ggml_vec_dot(v_z, q6b[3], v_y[3]);
 
-            isum += vec_hsum_i32x4(summs0) * scale[0] +
-                    vec_hsum_i32x4(summs1) * scale[1] +
-                    vec_hsum_i32x4(summs2) * scale[2] +
-                    vec_hsum_i32x4(summs3) * scale[3];
+            isum += (summs0[0] + summs0[1] + summs0[2] + summs0[3]) * scale[0] +
+                    (summs1[0] + summs1[1] + summs1[2] + summs1[3]) * scale[1] +
+                    (summs2[0] + summs2[1] + summs2[2] + summs2[3]) * scale[2] +
+                    (summs3[0] + summs3[1] + summs3[2] + summs3[3]) * scale[3];
 
             scale += 4;
 
@@ -1092,10 +1094,10 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
             summs2 = ggml_vec_dot(v_z, q6b[2], v_y[2]);
             summs3 = ggml_vec_dot(v_z, q6b[3], v_y[3]);
 
-            isum += vec_hsum_i32x4(summs0) * scale[0] +
-                    vec_hsum_i32x4(summs1) * scale[1] +
-                    vec_hsum_i32x4(summs2) * scale[2] +
-                    vec_hsum_i32x4(summs3) * scale[3];
+            isum += (summs0[0] + summs0[1] + summs0[2] + summs0[3]) * scale[0] +
+                    (summs1[0] + summs1[1] + summs1[2] + summs1[3]) * scale[1] +
+                    (summs2[0] + summs2[1] + summs2[2] + summs2[3]) * scale[2] +
+                    (summs3[0] + summs3[1] + summs3[2] + summs3[3]) * scale[3];
 
             scale += 4;
         }
@@ -1283,7 +1285,7 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const v
         const int8x16_t v_yh = vec_xl(QK8_0/2, y0->qs);
         const int32x4_t v_xy = ggml_vec_dot(ggml_vec_dot(vec_splats(0), v_xl, v_yl), v_xh, v_yh);
 
-        sumf += GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d) * vec_hsum_i32x4(v_xy);
+        sumf += GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d) * (v_xy[0] + v_xy[1] + v_xy[2] + v_xy[3]);
     }
 
     *s = sumf;
@@ -1352,8 +1354,8 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const v
 
             h >>= 4;
 
-            sumi1 += vec_hsum_i32x4(vsumi0) * ls1;
-            sumi2 += vec_hsum_i32x4(vsumi1) * ls2;
+            sumi1 += (vsumi0[0] + vsumi0[1] + vsumi0[2] + vsumi0[3]) * ls1;
+            sumi2 += (vsumi1[0] + vsumi1[1] + vsumi1[2] + vsumi1[3]) * ls2;
         }
 
         sumf += GGML_CPU_FP16_TO_FP32(x[ibl].d) * y[ibl].d * (sumi1 + sumi2);

ggml/src/ggml-cpu/common.h

@@ -28,14 +28,6 @@ static inline float bf16_to_f32(ggml_bf16_t x) {
     return GGML_BF16_TO_FP32(x);
 }
 
-static inline float i32_to_f32(int32_t x) {
-    return x;
-}
-
-static inline int32_t f32_to_i32(float x) {
-    return x;
-}
-
 static inline float f32_to_f32(float x) {
     return x;
 }
@@ -62,12 +54,6 @@ struct type_conversion_table<ggml_bf16_t> {
     static constexpr ggml_bf16_t (*from_f32)(float) = f32_to_bf16;
 };
 
-template <>
-struct type_conversion_table<int32_t> {
-    static constexpr float (*to_f32)(int32_t) = i32_to_f32;
-    static constexpr int32_t (*from_f32)(float) = f32_to_i32;
-};
-
 static std::pair<int64_t, int64_t> get_thread_range(const struct ggml_compute_params * params, const struct ggml_tensor * src0) {
     const int64_t ith = params->ith;
     const int64_t nth = params->nth;

ggml/src/ggml-cpu/ggml-cpu-impl.h

@@ -68,6 +68,12 @@ struct ggml_compute_params {
 #endif  // __VXE2__
 #endif  // __s390x__ && __VEC__
 
+#if defined(__s390x__) && defined(GGML_NNPA)
+#ifndef __NNPA__
+#define __NNPA__
+#endif  // __NNPA__
+#endif  // __s390x__ && GGML_NNPA
+
 #if defined(__ARM_FEATURE_SVE)
 #include <sys/prctl.h>
 #endif
@@ -483,16 +489,11 @@ inline static int16x8_t vec_padd_s16(int16x8_t a, int16x8_t b) {
 /**
  * @see https://github.com/ggml-org/llama.cpp/pull/14037
  */
-inline static float vec_hsum_f32x4(float32x4_t v) {
+inline float vec_hsum(float32x4_t v) {
     float32x4_t v_temp = v + vec_reve(v);
     return v_temp[0] + v_temp[1];
 }
 
-inline static int32_t vec_hsum_i32x4(int32x4_t v) {
-    int32x4_t v_temp = v + vec_reve(v);
-    return v_temp[0] + v_temp[1];
-}
-
 inline static int32x4_t ggml_vec_dot(int32x4_t acc, int8x16_t a, int8x16_t b) {
     const int16x8_t p = vec_mule(a, b) + vec_mulo(a, b);
     return acc + (vec_unpackh(p) + vec_unpackl(p));

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

@@ -373,9 +373,6 @@ static const struct ggml_type_traits_cpu type_traits_cpu[GGML_TYPE_COUNT] = {
         .vec_dot_type             = GGML_TYPE_Q8_K,
         .nrows                    = 1,
     },
-    [GGML_TYPE_I32] = {
-        .from_float               = (ggml_from_float_t) ggml_cpu_fp32_to_i32,
-    },
 };
 
 const struct ggml_type_traits_cpu * ggml_get_type_traits_cpu(enum ggml_type type) {
@@ -1879,10 +1876,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
             {
                 ggml_compute_forward_im2col_back_f32(params, tensor);
             } break;
-        case GGML_OP_IM2COL_3D:
-            {
-                ggml_compute_forward_im2col_3d(params, tensor);
-            } break;
         case GGML_OP_CONV_2D:
             {
                 ggml_compute_forward_conv_2d(params, tensor);
@@ -2262,7 +2255,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
             } break;
         case GGML_OP_IM2COL:
         case GGML_OP_IM2COL_BACK:
-        case GGML_OP_IM2COL_3D:
         case GGML_OP_CONV_2D:
         case GGML_OP_CONV_3D:
         case GGML_OP_CONV_2D_DW:
@@ -2699,10 +2691,7 @@ struct ggml_cplan ggml_graph_plan(
                         if (ggml_is_quantized(node->type) ||
                             // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32
                             (node->src[0]->type == GGML_TYPE_F16  && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) ||
-                            (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16) ||
-                            // conversion between F32 and I32
-                            (node->src[0]->type == GGML_TYPE_F32 && node->src[1] && node->src[1]->type == GGML_TYPE_I32) ||
-                            (node->src[0]->type == GGML_TYPE_I32 && node->src[1] && node->src[1]->type == GGML_TYPE_F32)) {
+                            (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) {
                             cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
                         }
                     } break;
@@ -3217,12 +3206,20 @@ void ggml_cpu_fp32_to_fp16(const float * x, ggml_fp16_t * y, int64_t n) {
         __m128i y_vec = _mm_cvtps_ph(x_vec, _MM_FROUND_TO_NEAREST_INT);
         _mm_storel_epi64((__m128i *)(y + i), y_vec);
     }
-#elif defined(__riscv_zvfh)
-    for (int vl; i < n; i += vl) {
-        vl = __riscv_vsetvl_e32m2(n - i);
-        vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl);
-        vfloat16m1_t vy = __riscv_vfncvt_f_f_w_f16m1(vx, vl);
-        __riscv_vse16_v_f16m1((_Float16 *)&y[i], vy, vl);
+#elif defined(__NNPA__)
+    for (; i + 7 < n; i += 8) {
+        float32x4_t v_xh = vec_xl(0, (const float *)(x + i + 0));
+        float32x4_t v_xl = vec_xl(0, (const float *)(x + i + 4));
+        uint16x8_t v_yd = vec_round_from_fp32(v_xh, v_xl, 0);
+        uint16x8_t v_y = vec_convert_to_fp16(v_yd, 0);
+        vec_xst(v_y, 0, (ggml_fp16_t *)(y + i));
+    }
+    for (; i + 3 < n; i += 4) {
+        float32x4_t v_x = vec_xl(0, (const float *)(x + i));
+        float32x4_t v_zero = vec_splats(0.0f);
+        uint16x8_t v_yd = vec_round_from_fp32(v_x, v_zero, 0);
+        uint16x8_t v_y = vec_convert_to_fp16(v_yd, 0);
+        vec_xst(v_y, 0, (ggml_fp16_t *)(y + i));
     }
 #endif
     for (; i < n; ++i) {
@@ -3250,6 +3247,21 @@ void ggml_cpu_fp16_to_fp32(const ggml_fp16_t * x, float * y, int64_t n) {
         __m128 y_vec = _mm_cvtph_ps(x_vec);
         _mm_storeu_ps(y + i, y_vec);
     }
+#elif defined(__NNPA__)
+    for (; i + 7 < n; i += 8) {
+        uint16x8_t v_x = vec_xl(0, (const ggml_fp16_t *)(x + i));
+        uint16x8_t v_yd = vec_convert_from_fp16(v_x, 0);
+        float32x4_t v_yh = vec_extend_to_fp32_hi(v_yd, 0);
+        float32x4_t v_yl = vec_extend_to_fp32_lo(v_yd, 0);
+        vec_xst(v_yh, 0, (float *)(y + i + 0));
+        vec_xst(v_yl, 0, (float *)(y + i + 4));
+    }
+    for (; i + 3 < n; i += 4) {
+        uint16x8_t v_x = vec_xl(0, (const ggml_fp16_t *)(x + i));
+        uint16x8_t v_yd = vec_convert_from_fp16(v_x, 0);
+        float32x4_t v_yh = vec_extend_to_fp32_hi(v_yd, 0);
+        vec_xst(v_yh, 0, (float *)(y + i));
+    }
 #endif
 
     for (; i < n; ++i) {
@@ -3264,13 +3276,6 @@ void ggml_cpu_fp32_to_bf16(const float * x, ggml_bf16_t * y, int64_t n) {
     }
 }
 
-void ggml_cpu_fp32_to_i32(const float * x, int32_t * y, int64_t n) {
-    int64_t i = 0;
-    for (; i < n; ++i) {
-        y[i] = x[i];
-    }
-}
-
 void ggml_cpu_bf16_to_fp32(const ggml_bf16_t * x, float * y, int64_t n) {
     int64_t i = 0;
 #if defined(__AVX2__)
@@ -3460,6 +3465,14 @@ int ggml_cpu_has_vxe(void) {
 #endif
 }
 
+int ggml_cpu_has_nnpa(void) {
+#if defined(GGML_NNPA)
+    return 1;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_neon(void) {
 #if defined(__ARM_ARCH) && defined(__ARM_NEON)
     return 1;

ggml/src/ggml-cpu/ggml-cpu.cpp

@@ -190,7 +190,6 @@ static const struct ggml_backend_i ggml_backend_cpu_i = {
     /* .graph_compute           = */ ggml_backend_cpu_graph_compute,
     /* .event_record            = */ NULL,
     /* .event_wait              = */ NULL,
-    /* .graph_optimize          = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_cpu_guid(void) {
@@ -349,10 +348,8 @@ static void ggml_backend_cpu_device_get_memory(ggml_backend_dev_t dev, size_t *
     long pages = sysconf(_SC_PHYS_PAGES);
     long page_size = sysconf(_SC_PAGE_SIZE);
     *total = pages * page_size;
-
-    // "free" system memory is ill-defined, for practical purposes assume that all of it is free:
     *free = *total;
-#endif // _WIN32
+#endif
 
     GGML_UNUSED(dev);
 }
@@ -579,6 +576,9 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r
         if (ggml_cpu_has_vxe()) {
             features.push_back({ "VXE", "1" });
         }
+        if (ggml_cpu_has_nnpa()) {
+            features.push_back({ "NNPA", "1" });
+        }
         if (ggml_cpu_has_wasm_simd()) {
             features.push_back({ "WASM_SIMD", "1" });
         }

ggml/src/ggml-cpu/kleidiai/kernels.cpp

@@ -14,7 +14,6 @@
 
 #include "kai_lhs_pack_bf16p2vlx2_f32_sme.h"
 #include "kai_lhs_quant_pack_qsi8d32p_f32.h"
-#include "kai_lhs_quant_pack_qsi8d32p4x8sb_f32_neon.h"
 #include "kai_lhs_quant_pack_qsi8d32p_f32_neon.h"
 
 #include "kai_rhs_pack_kxn_bf16p2vlx2b_f32_x32_sme.h"
@@ -128,12 +127,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1vlx4_qsi4c32p4vlx4_1vlx4vl_sme2_mopa,
         },
-        /* .gemm_lhs_info = */ {
-            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32_neon,
-            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32_neon,
-            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32_neon,
-            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32_neon,
-        },
         /* SME GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot,
@@ -148,7 +141,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4vlx4_1x4vl_sme2_sdot,
         },
-        /* .gemv_lhs_info = */ {
+        /* .lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32_neon,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32_neon,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32_neon,
@@ -180,12 +173,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
         },
-        /* .gemm_lhs_info = */ {
-            /* .get_offset            = */ kai_get_lhs_offset_lhs_pack_bf16p2vlx2_f32_sme,
-            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_pack_bf16p2vlx2_f32_sme,
-            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_pack_bf16p2vlx2_f32_sme,
-            /* .pack_func             = */ kai_run_lhs_pack_bf16p2vlx2_f32_sme,
-        },
         /* SME GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
@@ -200,7 +187,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_bf16p2vlx2_bf16p2vlx2_2vlx2vl_sme2_mopa,
         },
-        /* .gemv_lhs_info = */ {
+        /* .lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_pack_bf16p2vlx2_f32_sme,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_pack_bf16p2vlx2_f32_sme,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_pack_bf16p2vlx2_f32_sme,
@@ -235,12 +222,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod,
         },
-        /* .gemm_lhs_info = */ {
-            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
-            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
-            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
-            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
-        },
         /* DOTPROD GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
@@ -255,7 +236,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
         },
-        /* .gemv_lhs_info = */ {
+        /* .lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
@@ -289,12 +270,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm,
         },
-        /* .gemm_lhs_info = */ {
-            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
-            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
-            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
-            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
-        },
         /* i8mm GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
@@ -309,7 +284,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
         },
-        /* .gemv_lhs_info = */ {
+        /* .lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
@@ -344,12 +319,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p4x8_qsi4c32p4x8_16x4_neon_i8mm,
         },
-        /* .gemm_lhs_info = */ {
-            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
-            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
-            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
-            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p4x8sb_f32_neon,
-        },
         /* i8mm GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
@@ -364,7 +333,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x8_qsi4c32p4x8_1x4x32_neon_dotprod,
         },
-        /* .gemv_lhs_info = */ {
+        /* .lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
@@ -398,12 +367,6 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p4x4_qsi4c32p4x4_16x4_neon_dotprod,
         },
-        /* .gemm_lhs_info = */ {
-            /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
-            /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
-            /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,
-            /* .pack_func             = */ kai_run_lhs_quant_pack_qsi8d32p_f32,
-        },
         /* DOTPROD GEMV */
         /* .kern_info = */ {
             /* .get_m_step            = */ kai_get_m_step_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
@@ -418,7 +381,7 @@ static ggml_kleidiai_kernels gemm_gemv_kernels[] = {
             /* .get_dst_size          = */ kai_get_dst_size_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
             /* .run_kernel            = */ kai_run_matmul_clamp_f32_qsi8d32p1x4_qsi4c32p4x4_1x4_neon_dotprod,
         },
-        /* .gemv_lhs_info = */ {
+        /* .lhs_info = */ {
             /* .get_offset            = */ kai_get_lhs_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .get_packed_offset     = */ kai_get_lhs_packed_offset_lhs_quant_pack_qsi8d32p_f32,
             /* .packed_size           = */ kai_get_lhs_packed_size_lhs_quant_pack_qsi8d32p_f32,

ggml/src/ggml-cpu/kleidiai/kernels.h

@@ -84,11 +84,8 @@ struct rhs_packing_info {
 
 struct ggml_kleidiai_kernels {
     kernel_info gemm;
-    lhs_packing_info gemm_lhs_info;
-
     kernel_info gemv;
-    lhs_packing_info gemv_lhs_info;
-
+    lhs_packing_info lhs_info;
     rhs_packing_info rhs_info;
 
     cpu_feature required_cpu;

ggml/src/ggml-cpu/kleidiai/kleidiai.cpp

@@ -123,9 +123,7 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         }
         ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, op);
         GGML_ASSERT(kernels);
-        bool is_gemv = op->src[1]->ne[1] == 1;
-        kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm;
-        lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info;
+        kernel_info * kernel = op->src[1]->ne[1] == 1 ? &kernels->gemv : &kernels->gemm;
 
         size_t k = op->src[0]->ne[0];
         size_t n = op->src[0]->ne[1];
@@ -136,9 +134,9 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         size_t sr = kernel->get_sr();
 
         if (kernels->rhs_type == GGML_TYPE_Q4_0) {
-            size = variant_call<size_t>(lhs_info->packed_size, m, k, QK4_0, mr, kr, sr);
+            size = variant_call<size_t>(kernels->lhs_info.packed_size, m, k, QK4_0, mr, kr, sr);
         } else if (kernels->rhs_type == GGML_TYPE_F16) {
-            size = variant_call<size_t>(lhs_info->packed_size, m, k, mr, kr, sr) +
+            size = variant_call<size_t>(kernels->lhs_info.packed_size, m, k, mr, kr, sr) +
                    variant_call<size_t>(kernels->rhs_info.packed_size, n, k) +
                    k * n * sizeof(float) + n * sizeof(float);
         } else {
@@ -154,7 +152,7 @@ class tensor_traits : public ggml::cpu::tensor_traits {
             if (dst->src[0]->type == GGML_TYPE_Q4_0) {
                 return compute_forward_q4_0(params, dst);
             } else if (dst->src[0]->type == GGML_TYPE_F16) {
-                return compute_forward_fp16(params, dst);
+                return compute_forward_kv_cache(params, dst);
             }
         } else if (dst->op == GGML_OP_GET_ROWS) {
             if (dst->src[0]->type == GGML_TYPE_Q4_0) {
@@ -164,7 +162,7 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         return false;
     }
 
-    bool compute_forward_fp16(ggml_compute_params * params, struct ggml_tensor * dst) {
+    bool compute_forward_kv_cache(ggml_compute_params * params, struct ggml_tensor * dst) {
         static std::atomic_flag first_to_arrive = ATOMIC_FLAG_INIT;
 
         const ggml_tensor * src0 = dst->src[0];
@@ -175,9 +173,7 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst);
         GGML_ASSERT(kernels);
 
-        bool is_gemv = src1->ne[1] == 1;
-        kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm;
-        lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info;
+        kernel_info * kernel = src1->ne[1] == 1 ? &kernels->gemv : &kernels->gemm;
         GGML_ASSERT(kernel);
 
         const int nth = params->nth;
@@ -202,7 +198,7 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         const int64_t kr = static_cast<int64_t>(kernel->get_kr());
         const int64_t sr = static_cast<int64_t>(kernel->get_sr());
 
-        const size_t lhs_packed_size = variant_call<size_t>(lhs_info->packed_size, m, k, mr, kr, sr);
+        const size_t lhs_packed_size = variant_call<size_t>(kernels->lhs_info.packed_size, m, k, mr, kr, sr);
         const size_t rhs_packed_size = variant_call<size_t>(kernels->rhs_info.packed_size, n, k);
         const size_t kxn_size        = k * n * sizeof(float);
         const size_t bias_size       = n * sizeof(float);
@@ -233,12 +229,12 @@ class tensor_traits : public ggml::cpu::tensor_traits {
                     const int64_t num_m_per_thread = (ith == num_threads - 1) ? num_m_per_threadN_1 : num_m_per_thread0;
 
                     const size_t lhs_offset        = variant_call<size_t>(kernels->gemm.get_lhs_offset, m_start, lhs_stride);
-                    const size_t lhs_packed_offset = variant_call<size_t>(lhs_info->get_packed_offset, m_start, k, mr, kr, sr);
+                    const size_t lhs_packed_offset = variant_call<size_t>(kernels->lhs_info.get_packed_offset, m_start, k, mr, kr, sr);
 
                     const void * src_ptr = static_cast<const uint8_t *>(lhs_batch) + lhs_offset;
                     void * dst_ptr       = static_cast<uint8_t *>(lhs_packed) + lhs_packed_offset;
 
-                    variant_call<void>(lhs_info->pack_func, num_m_per_thread, k, mr, kr, sr, 0, src_ptr, lhs_stride, dst_ptr);
+                    variant_call<void>(kernels->lhs_info.pack_func, num_m_per_thread, k, mr, kr, sr, 0, src_ptr, lhs_stride, dst_ptr);
                 }
             }
 
@@ -310,9 +306,8 @@ class tensor_traits : public ggml::cpu::tensor_traits {
         ggml_kleidiai_kernels *kernels = ggml_kleidiai_select_kernels(ctx.features, dst);
         GGML_ASSERT(kernels);
 
-        bool is_gemv = src1->ne[1] == 1;
-        kernel_info * kernel = is_gemv ? &kernels->gemv : &kernels->gemm;
-        lhs_packing_info * lhs_info = is_gemv ? &kernels->gemv_lhs_info : &kernels->gemm_lhs_info;
+        kernel_info * kernel = src1->ne[1] == 1 ? &kernels->gemv : &kernels->gemm;
+        lhs_packing_info * lhs_info = &kernels->lhs_info;
 
         GGML_ASSERT(kernel);
 
@@ -515,6 +510,9 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
             op->src[0]->buffer &&
             (ggml_n_dims(op->src[0]) == 2) &&
             op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type() && ctx.kernels) {
+            if (op->op == GGML_OP_GET_ROWS && op->src[1]->ne[0] != 8) {
+                return false;
+            }
             if (op->src[1]->buffer && !ggml_backend_buft_is_host(op->src[1]->buffer->buft)) {
                 return false;
             }
@@ -531,8 +529,13 @@ class extra_buffer_type : ggml::cpu::extra_buffer_type {
             if (op->src[0]->buffer && op->src[0]->buffer->buft == ggml_backend_cpu_kleidiai_buffer_type()) {
                 return (ggml::cpu::tensor_traits *) op->src[0]->extra;
             }
-            else if (ggml_kleidiai_select_kernels(ctx.features, op) && op->src[1]->ne[1] > 1) {
-                if ((op->src[0]->nb[1] * op->src[0]->ne[1] != op->src[0]->nb[2]) ||
+            else if (ggml_kleidiai_select_kernels(ctx.features, op) &&
+                     op->src[0]->op == GGML_OP_VIEW &&
+                     (op->src[1]->op == GGML_OP_PERMUTE || op->src[1]->op ==  GGML_OP_SOFT_MAX) &&
+                     op->src[1]->ne[1] > 1) {
+                if ((op->src[0]->nb[0] != 2) ||
+                    (op->src[1]->nb[0] != 4) ||
+                    (op->src[0]->nb[1] * op->src[0]->ne[1] != op->src[0]->nb[2]) ||
                     (op->src[1]->nb[1] * op->src[1]->ne[1] != op->src[1]->nb[2])) {
                     return nullptr;
                 }

ggml/src/ggml-cpu/ops.h

@@ -69,7 +69,6 @@ void ggml_compute_forward_clamp(const struct ggml_compute_params * params, struc
 void ggml_compute_forward_conv_transpose_1d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_im2col(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_im2col_back_f32(const struct ggml_compute_params * params, struct ggml_tensor * dst);
-void ggml_compute_forward_im2col_3d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_conv_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_conv_3d(const struct ggml_compute_params * params, struct ggml_tensor * dst);
 void ggml_compute_forward_conv_transpose_2d(const struct ggml_compute_params * params, struct ggml_tensor * dst);

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

@@ -114,6 +114,26 @@ extern "C" {
     #define GGML_CPU_COMPUTE_FP32_TO_FP16(x) riscv_compute_fp32_to_fp16(x)
     #define GGML_CPU_FP16_TO_FP32(x) GGML_CPU_COMPUTE_FP16_TO_FP32(x)
     #define GGML_CPU_FP32_TO_FP16(x) GGML_CPU_COMPUTE_FP32_TO_FP16(x)
+#elif defined(__NNPA__)
+    #define GGML_CPU_COMPUTE_FP16_TO_FP32(x) nnpa_compute_fp16_to_fp32(x)
+    #define GGML_CPU_COMPUTE_FP32_TO_FP16(x) nnpa_compute_fp32_to_fp16(x)
+
+    #define GGML_CPU_FP16_TO_FP32(x) GGML_CPU_COMPUTE_FP16_TO_FP32(x)
+    #define GGML_CPU_FP32_TO_FP16(x) GGML_CPU_COMPUTE_FP32_TO_FP16(x)
+
+    static inline float nnpa_compute_fp16_to_fp32(ggml_fp16_t h) {
+        uint16x8_t v_h = vec_splats(h);
+        uint16x8_t v_hd = vec_convert_from_fp16(v_h, 0);
+        return vec_extend_to_fp32_hi(v_hd, 0)[0];
+    }
+
+    static inline ggml_fp16_t nnpa_compute_fp32_to_fp16(float f) {
+        float32x4_t v_f = vec_splats(f);
+        float32x4_t v_zero = vec_splats(0.0f);
+        uint16x8_t v_hd = vec_round_from_fp32(v_f, v_zero, 0);
+        uint16x8_t v_h = vec_convert_to_fp16(v_hd, 0);
+        return vec_extract(v_h, 0);
+    }
 #endif
 
 // precomputed f32 table for f16 (256 KB)
@@ -195,47 +215,6 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
 #define GGML_F32_VEC_MUL    GGML_F32xt_MUL
 #define GGML_F32_VEC_REDUCE GGML_F32xt_REDUCE
 
-// F16 SVE
-#define DEFAULT_PG32    svptrue_b32()
-#define DEFAULT_PG16    svptrue_b16()
-
-#define GGML_F32Cxt                         svfloat16_t
-#define GGML_F32Cxt_ZERO                    svdup_n_f16(0.0f)
-#define GGML_F32Cxt_SET1(x)                 svdup_n_f16(x)
-#define GGML_F32Cxt_LOAD(p)                 svld1_f16(DEFAULT_PG16, (const __fp16 *)(p))
-#define GGML_F32Cxt_STORE(dst_ptr, src_vec) svst1_f16(DEFAULT_PG16, (__fp16 *)(dst_ptr), (src_vec))
-
-#define GGML_F32Cxt_FMA_IMPL(pg, a, b, c)   svmad_f16_x(pg, b, c, a)
-#define GGML_F32Cxt_FMA(...)                GGML_F32Cxt_FMA_IMPL(DEFAULT_PG16, __VA_ARGS__)
-#define GGML_F32Cxt_ADD_IMPL(pg, a, b)      svadd_f16_x(pg, a, b)
-#define GGML_F32Cxt_ADD(...)                GGML_F32Cxt_ADD_IMPL(DEFAULT_PG16, __VA_ARGS__)
-#define GGML_F32Cxt_MUL_IMPL(pg, a, b)      svmul_f16_x(pg, a, b)
-#define GGML_F32Cxt_MUL(...)                GGML_F32Cxt_MUL_IMPL(DEFAULT_PG16, __VA_ARGS__)
-#define GGML_F32Cxt_REDUCE                  GGML_F16xt_REDUCE_MIXED
-
-#define GGML_F16x_VEC                GGML_F32Cxt
-#define GGML_F16x_VEC_ZERO           GGML_F32Cxt_ZERO
-#define GGML_F16x_VEC_SET1           GGML_F32Cxt_SET1
-#define GGML_F16x_VEC_LOAD(p, i)     GGML_F32Cxt_LOAD(p)
-#define GGML_F16x_VEC_STORE(p, r, i) GGML_F32Cxt_STORE((__fp16 *)(p), r)
-#define GGML_F16x_VEC_FMA            GGML_F32Cxt_FMA
-#define GGML_F16x_VEC_ADD            GGML_F32Cxt_ADD
-#define GGML_F16x_VEC_MUL            GGML_F32Cxt_MUL
-#define GGML_F16x_VEC_REDUCE         GGML_F32Cxt_REDUCE
-
-#define GGML_F16xt_REDUCE_ONE_IMPL(pg, a) svaddv_f16(pg, a)
-#define GGML_F16xt_REDUCE_ONE(...)        GGML_F16xt_REDUCE_ONE_IMPL(DEFAULT_PG16, __VA_ARGS__)
-
-#define GGML_F16xt_REDUCE_MIXED_IMPL(pg16, res, sum1, sum2, sum3, sum4)  \
-{                                                      \
-    sum1 = svadd_f16_x(pg16, sum1, sum2);              \
-    sum3 = svadd_f16_x(pg16, sum3, sum4);              \
-    sum1 = svadd_f16_x(pg16, sum1, sum3);              \
-    __fp16 sum_f16 = svaddv_f16(pg16, sum1);           \
-    (res) = (ggml_float) sum_f16;                      \
-}
-#define GGML_F16xt_REDUCE_MIXED(...) GGML_F16xt_REDUCE_MIXED_IMPL(DEFAULT_PG16, __VA_ARGS__)
-
 // F16 NEON
 
 #if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
@@ -1136,6 +1115,11 @@ static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) {
 #define GGML_F16_EPR  GGML_F32_EPR
 
 static inline float32x4_t __lzs_f16cx4_load(const ggml_fp16_t * x) {
+#if defined(__NNPA__)
+    uint16x8_t v_x = vec_xl(0, (const ggml_fp16_t *)x);
+    uint16x8_t v_xd = vec_convert_from_fp16(v_x, 0);
+    return vec_extend_to_fp32_hi(v_xd, 0);
+#else
     float tmp[4];
 
     for (int i = 0; i < 4; i++) {
@@ -1145,9 +1129,20 @@ static inline float32x4_t __lzs_f16cx4_load(const ggml_fp16_t * x) {
     // note: keep type-cast here to prevent compiler bugs
     // see: https://github.com/ggml-org/llama.cpp/issues/12846
     return vec_xl(0, (const float *)(tmp));
+#endif
 }
 
 static inline void __lzs_f16cx4_store(ggml_fp16_t * x, float32x4_t v_y) {
+#if defined(__NNPA__)
+    float32x4_t v_zero = vec_splats(0.0f);
+    uint16x8_t v_xd = vec_round_from_fp32(v_y, v_zero, 0);
+    uint16x8_t v_x = vec_convert_to_fp16(v_xd, 0);
+
+    x[0] = vec_extract(v_x, 0);
+    x[1] = vec_extract(v_x, 1);
+    x[2] = vec_extract(v_x, 2);
+    x[3] = vec_extract(v_x, 3);
+#else
     float arr[4];
 
     // note: keep type-cast here to prevent compiler bugs
@@ -1157,6 +1152,7 @@ static inline void __lzs_f16cx4_store(ggml_fp16_t * x, float32x4_t v_y) {
     for (int i = 0; i < 4; i++) {
         x[i] = GGML_CPU_FP32_TO_FP16(arr[i]);
     }
+#endif
 }
 
 #define GGML_F16_VEC                GGML_F32x4

ggml/src/ggml-cpu/vec.cpp

@@ -85,21 +85,15 @@ void ggml_vec_dot_f32(int n, float * GGML_RESTRICT s, size_t bs, const float * G
         // reduce sum1,sum2 to sum1
         GGML_F32_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4, sum5, sum6, sum7, sum8);
     #elif defined(__riscv_v_intrinsic)
-        int vl = __riscv_vsetvlmax_e32m8();
-        vfloat32m1_t vs = __riscv_vfmv_v_f_f32m1(0.0f, 1);
-        vfloat32m8_t vsum;
-        vfloat32m8_t ax;
-        vfloat32m8_t ay;
-        vsum = __riscv_vfmv_v_f_f32m8_tu(vsum, 0.0f, vl);
-        for (int i = 0; i < n; i += vl) {
-            vl = __riscv_vsetvl_e32m8(n - i);
-            ax = __riscv_vle32_v_f32m8_tu(ax, &x[i], vl);
-            ay = __riscv_vle32_v_f32m8_tu(ay, &y[i], vl);
-            vsum = __riscv_vfmacc_vv_f32m8_tu(vsum, ax, ay, vl);
+        vfloat32m1_t vsum = __riscv_vfmv_v_f_f32m1(0.0f, 1);
+        for (int i = 0, avl; i < n; i += avl) {
+            avl = __riscv_vsetvl_e32m8(n - i);
+            vfloat32m8_t ax = __riscv_vle32_v_f32m8(&x[i], avl);
+            vfloat32m8_t ay = __riscv_vle32_v_f32m8(&y[i], avl);
+            vfloat32m8_t prod = __riscv_vfmul_vv_f32m8(ax, ay, avl);
+            vsum = __riscv_vfredusum_vs_f32m8_f32m1(prod, vsum, avl);
         }
-        vl = __riscv_vsetvlmax_e32m8();
-        vs = __riscv_vfredusum_vs_f32m8_f32m1(vsum, vs, vl);
-        sumf += __riscv_vfmv_f_s_f32m1_f32(vs);
+        sumf += __riscv_vfmv_f_s_f32m1_f32(vsum);
     #else
         const int np = (n & ~(GGML_F32_STEP - 1));
 
@@ -213,125 +207,38 @@ void ggml_vec_dot_f16(int n, float * GGML_RESTRICT s, size_t bs, ggml_fp16_t * G
 
     ggml_float sumf = 0.0;
 
+#if defined(GGML_SIMD) && !defined(__riscv_v_intrinsic)
+    const int np = (n & ~(GGML_F16_STEP - 1));
 
-#if defined(GGML_SIMD)
-    #if defined(__ARM_FEATURE_SVE)
-        const int sve_register_length = svcntb() * 8; //get vector length
-        const int ggml_f16_epr = sve_register_length / 16; // running when 16
-        const int ggml_f16_step = 8 * ggml_f16_epr; // choose 8 SVE registers
+    GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO };
 
-        const int np= (n & ~(ggml_f16_step - 1));
-        svfloat16_t sum1 = svdup_n_f16(0.0f);
-        svfloat16_t sum2 = svdup_n_f16(0.0f);
-        svfloat16_t sum3 = svdup_n_f16(0.0f);
-        svfloat16_t sum4 = svdup_n_f16(0.0f);
+    GGML_F16_VEC ax[GGML_F16_ARR];
+    GGML_F16_VEC ay[GGML_F16_ARR];
 
-        svfloat16_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
-        svfloat16_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
-        for (int i = 0; i < np; i += ggml_f16_step) {
-            ax1 = GGML_F16x_VEC_LOAD(x + i + 0 * ggml_f16_epr, 0);
-            ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0);
-            sum1 = GGML_F16x_VEC_FMA(sum1, ax1, ay1);
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
 
-            ax2 = GGML_F16x_VEC_LOAD(x + i + 1 * ggml_f16_epr, 1);
-            ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1);
-            sum2 = GGML_F16x_VEC_FMA(sum2, ax2, ay2);
-
-            ax3 = GGML_F16x_VEC_LOAD(x + i + 2 * ggml_f16_epr, 2);
-            ay3 = GGML_F16x_VEC_LOAD(y + i + 2 * ggml_f16_epr, 2);
-            sum3 = GGML_F16x_VEC_FMA(sum3, ax3, ay3);
-
-            ax4 = GGML_F16x_VEC_LOAD(x + i + 3 * ggml_f16_epr, 3);
-            ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3);
-            sum4 = GGML_F16x_VEC_FMA(sum4, ax4, ay4);
-
-            ax5 = GGML_F16x_VEC_LOAD(x + i + 4 * ggml_f16_epr, 4);
-            ay5 = GGML_F16x_VEC_LOAD(y + i + 4 * ggml_f16_epr, 4);
-            sum1 = GGML_F16x_VEC_FMA(sum1, ax5, ay5);
-
-            ax6 = GGML_F16x_VEC_LOAD(x + i + 5 * ggml_f16_epr, 5);
-            ay6 = GGML_F16x_VEC_LOAD(y + i + 5 * ggml_f16_epr, 5);
-            sum2 = GGML_F16x_VEC_FMA(sum2, ax6, ay6);
-
-            ax7 = GGML_F16x_VEC_LOAD(x + i + 6 * ggml_f16_epr, 6);
-            ay7 = GGML_F16x_VEC_LOAD(y + i + 6 * ggml_f16_epr, 6);
-            sum3 = GGML_F16x_VEC_FMA(sum3, ax7, ay7);
-
-            ax8 = GGML_F16x_VEC_LOAD(x + i + 7 * ggml_f16_epr, 7);
-            ay8 = GGML_F16x_VEC_LOAD(y + i + 7 * ggml_f16_epr, 7);
-            sum4 = GGML_F16x_VEC_FMA(sum4, ax8, ay8);
+            sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]);
         }
+    }
 
-        const int np2 = (n & ~(ggml_f16_epr - 1)); // round down to multiple of 8
-        for (int k = np; k < np2; k += ggml_f16_epr) {
-            svfloat16_t rx = GGML_F16x_VEC_LOAD(x + k, 0);
-            svfloat16_t ry = GGML_F16x_VEC_LOAD(y + k, 0);
-            sum1 = GGML_F16x_VEC_FMA(sum1, rx, ry);
-        }
+    // reduce sum0..sum3 to sum0
+    GGML_F16_VEC_REDUCE(sumf, sum);
 
-        if (np2 < n) {
-            svbool_t pg = svwhilelt_b16(np2, n);
-            svfloat16_t hx = svld1_f16(pg, (const __fp16 *)(x + np2));
-            svfloat16_t hy = svld1_f16(pg, (const __fp16 *)(y + np2));
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        sumf += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[i])*GGML_CPU_FP16_TO_FP32(y[i]));
+    }
 
-            sum1 = svmad_f16_x(pg, hx, hy, sum1);
-        }
-        GGML_F16x_VEC_REDUCE(sumf, sum1, sum2, sum3, sum4);
-    #elif defined(__riscv_v_intrinsic)
-        #if defined(__riscv_zvfh)
-            int vl = __riscv_vsetvlmax_e32m2();
-            vfloat32m1_t vs = __riscv_vfmv_v_f_f32m1(0.0f, 1);
-            vfloat32m2_t vsum;
-            vfloat16m1_t ax;
-            vfloat16m1_t ay;
-            vsum = __riscv_vreinterpret_v_u32m2_f32m2(__riscv_vmv_v_x_u32m2(0, vl));
-            for (int i = 0; i < n; i += vl) {
-                vl = __riscv_vsetvl_e16m1(n - i);
-                ax = __riscv_vle16_v_f16m1_tu(ax, (const _Float16 *)&x[i], vl);
-                ay = __riscv_vle16_v_f16m1_tu(ay, (const _Float16 *)&y[i], vl);
-                vsum = __riscv_vfwmacc_vv_f32m2_tu(vsum, ax, ay, vl);
-            }
-            vl = __riscv_vsetvlmax_e32m1();
-            vfloat32m1_t ac0 = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m2_f32m1(vsum, 0), __riscv_vget_v_f32m2_f32m1(vsum, 1), vl);
-            vs = __riscv_vfredusum_vs_f32m1_f32m1(ac0, vs, vl);
-            sumf += __riscv_vfmv_f_s_f32m1_f32(vs);
-        #else
-            for (int i = 0; i < n; ++i) {
-                sumf += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[i])*GGML_CPU_FP16_TO_FP32(y[i]));
-            }
-        #endif // __riscv_zvfh
-    #else
-        const int np = (n & ~(GGML_F16_STEP - 1));
-
-        GGML_F16_VEC sum[GGML_F16_ARR] = { GGML_F16_VEC_ZERO };
-
-        GGML_F16_VEC ax[GGML_F16_ARR];
-        GGML_F16_VEC ay[GGML_F16_ARR];
-
-        for (int i = 0; i < np; i += GGML_F16_STEP) {
-            for (int j = 0; j < GGML_F16_ARR; j++) {
-                ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
-                ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
-
-                sum[j] = GGML_F16_VEC_FMA(sum[j], ax[j], ay[j]);
-            }
-        }
-
-        // reduce sum0..sum3 to sum0
-        GGML_F16_VEC_REDUCE(sumf, sum);
-
-        // leftovers
-        for (int i = np; i < n; ++i) {
-            sumf += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[i])*GGML_CPU_FP16_TO_FP32(y[i]));
-        }
-        // if you hit this, you are likely running outside the FP range
-        assert(!isnan(sumf) && !isinf(sumf));
-    #endif
+    // if you hit this, you are likely running outside the FP range
+    assert(!isnan(sumf) && !isinf(sumf));
 #else
     for (int i = 0; i < n; ++i) {
         sumf += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[i])*GGML_CPU_FP16_TO_FP32(y[i]));
     }
-#endif // GGML_SIMD
+#endif
 
     *s = sumf;
 }
@@ -350,12 +257,6 @@ void ggml_vec_silu_f32(const int n, float * y, const float * x) {
     for (; i + 3 < n; i += 4) {
         _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i)));
     }
-#elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
-    const int vlen = svcntw();
-    for (; i < n; i += vlen) {
-        const svbool_t pg = svwhilelt_b32_s32(i, n);
-        svst1_f32(pg, y + i, ggml_v_silu(pg, svld1_f32(pg, x + i)));
-    }
 #elif defined(__ARM_NEON) && defined(__aarch64__)
     for (; i + 3 < n; i += 4) {
         vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i)));
@@ -380,24 +281,10 @@ void ggml_vec_swiglu_f32(const int n, float * y, const float * x, const float *
     for (; i + 3 < n; i += 4) {
         _mm_storeu_ps(y + i, _mm_mul_ps(ggml_v_silu(_mm_loadu_ps(x + i)), _mm_loadu_ps(g + i)));
     }
-#elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
-    const int vlen = svcntw();
-    for (; i < n; i += vlen) {
-        const svbool_t pg = svwhilelt_b32_s32(i, n);
-        svst1_f32(pg, y + i, svmul_f32_x(pg, ggml_v_silu(pg, svld1_f32(pg, x + i)), svld1_f32(pg, g + i)));
-    }
 #elif defined(__ARM_NEON) && defined(__aarch64__)
     for (; i + 3 < n; i += 4) {
         vst1q_f32(y + i, vmulq_f32(ggml_v_silu(vld1q_f32(x + i)), vld1q_f32(g + i)));
     }
-#elif defined(__riscv_v_intrinsic)
-    for (int vl; i < n; i += vl) {
-        vl = __riscv_vsetvl_e32m2(n - i);
-        vfloat32m2_t vx = __riscv_vle32_v_f32m2(&x[i], vl);
-        vfloat32m2_t vg = __riscv_vle32_v_f32m2(&g[i], vl);
-        vfloat32m2_t vy = __riscv_vfmul_vv_f32m2(ggml_v_silu_m2(vx, vl), vg, vl);
-        __riscv_vse32_v_f32m2(&y[i], vy, vl);
-    }
 #endif
     for (; i < n; ++i) {
         y[i] = ggml_silu_f32(x[i]) * g[i];
@@ -441,15 +328,6 @@ ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float
 #endif
         sum += (ggml_float)_mm_cvtss_f32(val);
     }
-#elif defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
-    const int vlen = svcntw();
-    for (; i < n; i += vlen) {
-        const svbool_t pg = svwhilelt_b32_s32(i, n);
-        svfloat32_t val = ggml_v_expf(pg, svsub_f32_x(pg, svld1_f32(pg, x + i),
-                                                svdup_n_f32_x(pg, max)));
-        svst1_f32(pg, y + i, val);
-        sum += (ggml_float)svaddv_f32(pg, val);
-    }
 #elif defined(__ARM_NEON) && defined(__aarch64__)
     for (; i + 3 < n; i += 4) {
         float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i),

ggml/src/ggml-cpu/vec.h

@@ -119,149 +119,45 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
     }
 
 #if defined(GGML_SIMD)
-    #if defined(__ARM_FEATURE_SVE)
-
-        const int sve_register_length = svcntb() * 8;
-        const int ggml_f16_epr = sve_register_length / 16; // running when 16
-        const int ggml_f16_step = 8 * ggml_f16_epr; // choose 8 SVE registers
-
-        const int np = (n & ~(ggml_f16_step - 1));
-
-        svfloat16_t sum_00 = svdup_n_f16(0.0f);
-        svfloat16_t sum_01 = svdup_n_f16(0.0f);
-        svfloat16_t sum_02 = svdup_n_f16(0.0f);
-        svfloat16_t sum_03 = svdup_n_f16(0.0f);
-
-        svfloat16_t sum_10 = svdup_n_f16(0.0f);
-        svfloat16_t sum_11 = svdup_n_f16(0.0f);
-        svfloat16_t sum_12 = svdup_n_f16(0.0f);
-        svfloat16_t sum_13 = svdup_n_f16(0.0f);
-
-        svfloat16_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
-        svfloat16_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
-
-        for (int i = 0; i < np; i += ggml_f16_step) {
-            ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0); // 8 elements
-
-            ax1 = GGML_F16x_VEC_LOAD(x[0] + i + 0*ggml_f16_epr, 0); // 8 elemnst
-            sum_00 = GGML_F16x_VEC_FMA(sum_00, ax1, ay1);     // sum_00 = sum_00+ax1*ay1
-            ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 0*ggml_f16_epr, 0); // 8 elements
-            sum_10 = GGML_F16x_VEC_FMA(sum_10, ax1, ay1);
-
-            ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1); // next 8 elements
-
-            ax2 = GGML_F16x_VEC_LOAD(x[0] + i + 1*ggml_f16_epr, 1); // next 8 ekements
-            sum_01 = GGML_F16x_VEC_FMA(sum_01, ax2, ay2);
-            ax2 = GGML_F16x_VEC_LOAD(x[1] + i + 1*ggml_f16_epr, 1);
-            sum_11 = GGML_F16x_VEC_FMA(sum_11, ax2, ay2);
-
-            ay3 = GGML_F16x_VEC_LOAD(y + i + 2 * ggml_f16_epr, 2);
-
-            ax3 = GGML_F16x_VEC_LOAD(x[0] + i + 2*ggml_f16_epr, 2);
-            sum_02 = GGML_F16x_VEC_FMA(sum_02, ax3, ay3);
-            ax1 = GGML_F16x_VEC_LOAD(x[1] + i + 2*ggml_f16_epr, 2);
-            sum_12 = GGML_F16x_VEC_FMA(sum_12, ax3, ay3);
-
-            ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3);
-
-            ax4 = GGML_F16x_VEC_LOAD(x[0] + i + 3*ggml_f16_epr, 3);
-            sum_03 = GGML_F16x_VEC_FMA(sum_03, ax4, ay4);
-            ax4 = GGML_F16x_VEC_LOAD(x[1] + i + 3*ggml_f16_epr, 3);
-            sum_13 = GGML_F16x_VEC_FMA(sum_13, ax4, ay4);
-
-            ay5 = GGML_F16x_VEC_LOAD(y + i + 4 * ggml_f16_epr, 4);
-
-            ax5 = GGML_F16x_VEC_LOAD(x[0] + i + 4*ggml_f16_epr, 4);
-
-            sum_00 = GGML_F16x_VEC_FMA(sum_00, ax5, ay5);
-            ax5 = GGML_F16x_VEC_LOAD(x[1] + i + 4*ggml_f16_epr, 4);
-            sum_10 = GGML_F16x_VEC_FMA(sum_10, ax5, ay5);
-
-            ay6 = GGML_F16x_VEC_LOAD(y + i + 5 * ggml_f16_epr, 5);
-
-            ax6 = GGML_F16x_VEC_LOAD(x[0] + i + 5*ggml_f16_epr, 5);
-
-            sum_01 = GGML_F16x_VEC_FMA(sum_01, ax6, ay6);
-            ax6 = GGML_F16x_VEC_LOAD(x[1] + i + 5*ggml_f16_epr, 5);
-            sum_11 = GGML_F16x_VEC_FMA(sum_11, ax6, ay6);
-
-            ay7 = GGML_F16x_VEC_LOAD(y + i + 6 * ggml_f16_epr, 6);
-
-            ax7 = GGML_F16x_VEC_LOAD(x[0] + i + 6*ggml_f16_epr, 6);
-
-            sum_02 = GGML_F16x_VEC_FMA(sum_02, ax7, ay7);
-            ax7 = GGML_F16x_VEC_LOAD(x[1] + i + 6*ggml_f16_epr, 6);
-            sum_12 = GGML_F16x_VEC_FMA(sum_12, ax7, ay7);
-
-            ay8 = GGML_F16x_VEC_LOAD(y + i + 7 * ggml_f16_epr, 7);
-
-            ax8 = GGML_F16x_VEC_LOAD(x[0] + i + 7*ggml_f16_epr, 7);
-
-            sum_03 = GGML_F16x_VEC_FMA(sum_03, ax8, ay8);
-            ax8 = GGML_F16x_VEC_LOAD(x[1] + i + 7*ggml_f16_epr, 7);
-            sum_13 = GGML_F16x_VEC_FMA(sum_13, ax8, ay8);
+#if defined(__riscv_v_intrinsic)
+    // todo: RVV impl
+    for (int i = 0; i < n; ++i) {
+        for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
+            sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
         }
+    }
+#else
+    const int np = (n & ~(GGML_F16_STEP - 1));
 
-        const int np2 = (n & ~(ggml_f16_epr - 1));
-        for (int k = np; k < np2; k += ggml_f16_epr) {
-            svfloat16_t ry = GGML_F16x_VEC_LOAD(y + k, 0);
+    GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } };
 
-            svfloat16_t rx = GGML_F16x_VEC_LOAD(x[0] + k, 0);
-            sum_00 = GGML_F16x_VEC_FMA(sum_00, rx, ry);
-            rx = GGML_F16x_VEC_LOAD(x[1] + k, 0);
-            sum_10 = GGML_F16x_VEC_FMA(sum_10, rx, ry);
-        }
+    GGML_F16_VEC ax[GGML_F16_ARR];
+    GGML_F16_VEC ay[GGML_F16_ARR];
 
-        if (np2 < n) {
-            svbool_t pg = svwhilelt_b16(np2, n);
-            svfloat16_t hx_0 = svld1_f16(pg, (const __fp16 *)(x[0] + np2));
-            svfloat16_t hx_1 = svld1_f16(pg, (const __fp16 *)(x[1] + np2));
-            svfloat16_t hy = svld1_f16(pg, (const __fp16 *)(y + np2));
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
 
-            sum_00 = svmad_f16_x(pg, hx_0, hy, sum_00);
-            sum_10 = svmad_f16_x(pg, hx_1, hy, sum_10);
-        }
-        GGML_F16x_VEC_REDUCE(sumf[0], sum_00, sum_01, sum_02, sum_03);
-        GGML_F16x_VEC_REDUCE(sumf[1], sum_10, sum_11, sum_12, sum_13);
-    #elif defined(__riscv_v_intrinsic)
-      // todo: RVV impl
-      for (int i = 0; i < n; ++i) {
-          for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
-              sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
-          }
-      }
-    #else
-        const int np = (n & ~(GGML_F16_STEP - 1));
+            for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
+                ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j);
 
-        GGML_F16_VEC sum[GGML_VEC_DOT_UNROLL][GGML_F16_ARR] = { { GGML_F16_VEC_ZERO } };
-
-        GGML_F16_VEC ax[GGML_F16_ARR];
-        GGML_F16_VEC ay[GGML_F16_ARR];
-
-        for (int i = 0; i < np; i += GGML_F16_STEP) {
-            for (int j = 0; j < GGML_F16_ARR; j++) {
-                ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
-
-                for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
-                    ax[j] = GGML_F16_VEC_LOAD(x[k] + i + j*GGML_F16_EPR, j);
-
-                    sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]);
-                }
+                sum[k][j] = GGML_F16_VEC_FMA(sum[k][j], ax[j], ay[j]);
             }
         }
+    }
 
-        // reduce sum0..sum3 to sum0
-        for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
-            GGML_F16_VEC_REDUCE(sumf[k], sum[k]);
-        }
+    // reduce sum0..sum3 to sum0
+    for (int k = 0; k < GGML_VEC_DOT_UNROLL; ++k) {
+        GGML_F16_VEC_REDUCE(sumf[k], sum[k]);
+    }
 
-        // leftovers
-        for (int i = np; i < n; ++i) {
-            for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
-                sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
-            }
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
+            sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
         }
-    #endif
+    }
+#endif
 #else
     for (int i = 0; i < n; ++i) {
         for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
@@ -397,112 +293,35 @@ inline static void ggml_vec_mad_f32(const int n, float * GGML_RESTRICT y, const
 
 inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y, const ggml_fp16_t * GGML_RESTRICT x, const float v) {
 #if defined(GGML_SIMD)
-    #if defined(__ARM_FEATURE_SVE)
-        const int sve_register_length = svcntb() * 8;
-        const int ggml_f16_epr = sve_register_length / 16;
-        const int ggml_f16_step = 8 * ggml_f16_epr;
+#if defined(__riscv_v_intrinsic)
+    // todo: RVV impl
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
+    }
+#else
+    const int np = (n & ~(GGML_F16_STEP - 1));
 
-        GGML_F16x_VEC vx = GGML_F16x_VEC_SET1(v);
+    GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
 
-        const int np= (n & ~(ggml_f16_step - 1));
+    GGML_F16_VEC ax[GGML_F16_ARR];
+    GGML_F16_VEC ay[GGML_F16_ARR];
 
-        svfloat16_t ax1, ax2, ax3, ax4, ax5, ax6, ax7, ax8;
-        svfloat16_t ay1, ay2, ay3, ay4, ay5, ay6, ay7, ay8;
-        for (int i = 0; i < np; i += ggml_f16_step) {
-            ax1 = GGML_F16x_VEC_LOAD(x + i + 0 * ggml_f16_epr, 0);
-            ay1 = GGML_F16x_VEC_LOAD(y + i + 0 * ggml_f16_epr, 0);
-            ay1 = GGML_F16x_VEC_FMA(ay1, ax1, vx);
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx);
 
-            GGML_F16x_VEC_STORE(y + i + 0 * ggml_f16_epr, ay1, 0);
-
-            ax2 = GGML_F16x_VEC_LOAD(x + i + 1 * ggml_f16_epr, 1);
-            ay2 = GGML_F16x_VEC_LOAD(y + i + 1 * ggml_f16_epr, 1);
-            ay2 = GGML_F16x_VEC_FMA(ay2, ax2, vx);
-
-            GGML_F16x_VEC_STORE(y + i + 1 * ggml_f16_epr, ay2, 1);
-
-            ax3 = GGML_F16x_VEC_LOAD(x + i + 2 * ggml_f16_epr, 2);
-            ay3 = GGML_F16x_VEC_LOAD(y + i + 2 * ggml_f16_epr, 2);
-            ay3 = GGML_F16x_VEC_FMA(ay3, ax3, vx);
-
-            GGML_F16x_VEC_STORE(y + i + 2 * ggml_f16_epr, ay3, 2);
-
-            ax4 = GGML_F16x_VEC_LOAD(x + i + 3 * ggml_f16_epr, 3);
-            ay4 = GGML_F16x_VEC_LOAD(y + i + 3 * ggml_f16_epr, 3);
-            ay4 = GGML_F16x_VEC_FMA(ay4, ax4, vx);
-
-            GGML_F16x_VEC_STORE(y + i + 3 * ggml_f16_epr, ay4, 3);
-
-            ax5 = GGML_F16x_VEC_LOAD(x + i + 4 * ggml_f16_epr, 4);
-            ay5 = GGML_F16x_VEC_LOAD(y + i + 4 * ggml_f16_epr, 4);
-            ay5 = GGML_F16x_VEC_FMA(ay5, ax5, vx);
-
-            GGML_F16x_VEC_STORE(y + i + 4 * ggml_f16_epr, ay5, 4);
-
-            ax6 = GGML_F16x_VEC_LOAD(x + i + 5 * ggml_f16_epr, 5);
-            ay6 = GGML_F16x_VEC_LOAD(y + i + 5 * ggml_f16_epr, 5);
-            ay6 = GGML_F16x_VEC_FMA(ay6, ax6, vx);
-
-            GGML_F16x_VEC_STORE(y + i + 5 * ggml_f16_epr, ay6, 5);
-
-            ax7 = GGML_F16x_VEC_LOAD(x + i + 6 * ggml_f16_epr, 6);
-            ay7 = GGML_F16x_VEC_LOAD(y + i + 6 * ggml_f16_epr, 6);
-            ay7 = GGML_F16x_VEC_FMA(ay7, ax7, vx);
-
-            GGML_F16x_VEC_STORE(y + i + 6 * ggml_f16_epr, ay7, 6);
-
-            ax8 = GGML_F16x_VEC_LOAD(x + i + 7 * ggml_f16_epr, 7);
-            ay8 = GGML_F16x_VEC_LOAD(y + i + 7 * ggml_f16_epr, 7);
-            ay8 = GGML_F16x_VEC_FMA(ay8, ax8, vx);
-
-            GGML_F16x_VEC_STORE(y + i + 7 * ggml_f16_epr, ay8, 7);
+            GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
         }
-        const int np2 = (n & ~(ggml_f16_epr - 1));
-        for (int k = np; k < np2; k += ggml_f16_epr) {
-            svfloat16_t rx = GGML_F16x_VEC_LOAD(x + k, 0);
-            svfloat16_t ry = GGML_F16x_VEC_LOAD(y + k, 0);
-            ry = GGML_F16x_VEC_FMA(ry, rx, vx);
+    }
 
-            GGML_F16x_VEC_STORE(y + k, ry, 0);
-        }
-
-        if (np2 < n) {
-            svbool_t pg = svwhilelt_b16(np2, n);
-            svfloat16_t hx = svld1_f16(pg, (const __fp16 *)(x + np2));
-            svfloat16_t hy = svld1_f16(pg, (const __fp16 *)(y + np2));
-            hy = svmad_f16_x(pg, hx, vx, hy);
-            svst1_f16(pg, (__fp16 *)(y + np2), hy);
-        }
-
-    #elif defined(__riscv_v_intrinsic)
-        // todo: RVV impl
-        // scalar
-        for (int i = 0; i < n; ++i) {
-            y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
-        }
-    #else
-        const int np = (n & ~(GGML_F16_STEP - 1));
-
-        GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
-
-        GGML_F16_VEC ax[GGML_F16_ARR];
-        GGML_F16_VEC ay[GGML_F16_ARR];
-
-        for (int i = 0; i < np; i += GGML_F16_STEP) {
-            for (int j = 0; j < GGML_F16_ARR; j++) {
-                ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j);
-                ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
-                ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx);
-
-                GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
-            }
-        }
-
-        // leftovers
-        for (int i = np; i < n; ++i) {
-            y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
-        }
-    #endif
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i]) + GGML_CPU_FP16_TO_FP32(x[i])*v);
+    }
+#endif
 #else
     // scalar
     for (int i = 0; i < n; ++i) {
@@ -698,59 +517,33 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float   v) {
 
 inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) {
 #if defined(GGML_SIMD)
-    #if defined(__ARM_FEATURE_SVE)
-        const int sve_register_length = svcntb() * 8;
-        const int ggml_f16_epr = sve_register_length / 16;
-        const int ggml_f16_step = 2 * ggml_f16_epr;
+#if defined(__riscv_v_intrinsic)
+    // todo: RVV impl
+    // scalar
+    for (int i = 0; i < n; ++i) {
+        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
+    }
+#else
+    const int np = (n & ~(GGML_F16_STEP - 1));
 
-        GGML_F16x_VEC vx =  GGML_F16x_VEC_SET1(v);
-        const int np = (n & ~(ggml_f16_step - 1));
-        svfloat16_t ay1, ay2;
+    GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
 
-        for (int i = 0; i < np; i += ggml_f16_step) {
-            ay1 = GGML_F16x_VEC_LOAD(y + i + 0*ggml_f16_epr, 0);
-            ay1 = GGML_F16x_VEC_MUL(ay1, vx);
-            GGML_F16x_VEC_STORE(y + i + 0*ggml_f16_epr, ay1, 0);
+    GGML_F16_VEC ay[GGML_F16_ARR];
 
-            ay2 = GGML_F16x_VEC_LOAD(y + i + 1*ggml_f16_epr, 1);
-            ay2 = GGML_F16x_VEC_MUL(ay2, vx);
-            GGML_F16x_VEC_STORE(y + i + 1*ggml_f16_epr, ay2, 1);
+    for (int i = 0; i < np; i += GGML_F16_STEP) {
+        for (int j = 0; j < GGML_F16_ARR; j++) {
+            ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
+            ay[j] = GGML_F16_VEC_MUL(ay[j], vx);
+
+            GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
         }
-        // leftovers
-        // maximum number of leftover elements will be less that ggmlF_16x_epr. Apply predicated svmad on available elements only
-        if (np < n) {
-            svbool_t pg = svwhilelt_b16(np, n);
-            svfloat16_t hy = svld1_f16(pg, (__fp16 *)(y + np));
-            svfloat16_t out = svmul_f16_m(pg, hy, vx);
-            svst1_f16(pg, (__fp16 *)(y + np), out);
-        }
-    #elif defined(__riscv_v_intrinsic)
-        // todo: RVV impl
-        // scalar
-        for (int i = 0; i < n; ++i) {
-            y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
-        }
-    #else
-        const int np = (n & ~(GGML_F16_STEP - 1));
+    }
 
-        GGML_F16_VEC vx = GGML_F16_VEC_SET1(v);
-
-        GGML_F16_VEC ay[GGML_F16_ARR];
-
-        for (int i = 0; i < np; i += GGML_F16_STEP) {
-            for (int j = 0; j < GGML_F16_ARR; j++) {
-                ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j);
-                ay[j] = GGML_F16_VEC_MUL(ay[j], vx);
-
-                GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j);
-            }
-        }
-
-        // leftovers
-        for (int i = np; i < n; ++i) {
-            y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
-        }
-    #endif
+    // leftovers
+    for (int i = np; i < n; ++i) {
+        y[i] = GGML_CPU_FP32_TO_FP16(GGML_CPU_FP16_TO_FP32(y[i])*v);
+    }
+#endif
 #else
     // scalar
     for (int i = 0; i < n; ++i) {
@@ -1002,39 +795,7 @@ https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_cpu/sr
     }
 #endif
 
-#if defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
-
-inline static svfloat32_t ggml_v_expf(svbool_t pg, svfloat32_t x) {
-    const svfloat32_t r = svdup_n_f32_x(pg, 0x1.8p23f);
-    const svfloat32_t z = svmla_n_f32_x(pg, r, x, 0x1.715476p+0f);
-    const svfloat32_t n = svsub_f32_x(pg, z, r);
-    const svfloat32_t b = svmls_n_f32_x(pg, svmls_n_f32_x(pg, x, n, 0x1.62e4p-1f), n, 0x1.7f7d1cp-20f);
-    const svuint32_t e = svlsl_n_u32_x(pg, svreinterpret_u32_f32(z), 23);
-    const svfloat32_t k = svreinterpret_f32_u32(svadd_u32_x(pg, e, svreinterpret_u32_f32(svdup_n_f32_x(pg, 1))));
-    const svbool_t c = svacgt_n_f32(pg, n, 126);
-    const svfloat32_t u = svmul_f32_x(pg, b, b);
-    const svfloat32_t j = svmla_f32_x(pg,
-        svmul_n_f32_x(pg, b, 0x1.ffffecp-1f),
-        svmla_f32_x(pg, svmla_f32_x(pg, svdup_n_f32_x(pg, 0x1.fffdb6p-2f), svdup_n_f32_x(pg, 0x1.555e66p-3f), b),
-                        svmla_f32_x(pg, svdup_n_f32_x(pg, 0x1.573e2ep-5f), svdup_n_f32_x(pg, 0x1.0e4020p-7f), b), u), u);
-    const svuint32_t d = svdup_n_u32_z(svcmple_n_f32(pg, n, 0.0), 0x82000000);
-    const svfloat32_t s1 = svreinterpret_f32_u32(svadd_n_u32_x(pg, d, 0x7f000000));
-    const svfloat32_t s2 = svreinterpret_f32_u32(svsub_u32_x(pg, e, d));
-    return svsel_f32(svacgt_f32(pg, n, svdup_n_f32_x(pg, 192)), svmul_f32_x(pg, s1, s1),
-                     svsel_f32(c, svmul_f32_x(pg, svmla_f32_x(pg, s2, s2, j), s1), svmla_f32_x(pg, k, k, j)));
-}
-
-// computes silu x/(1+exp(-x)) in single precision vector
-inline static svfloat32_t ggml_v_silu(svbool_t pg, svfloat32_t x) {
-    const svfloat32_t one = svdup_n_f32_x(pg, 1.0f);
-    const svfloat32_t zero = svdup_n_f32_x(pg, 0.0f);
-    const svfloat32_t neg_x = svsub_f32_x(pg, zero, x);
-    const svfloat32_t exp_neg_x = ggml_v_expf(pg, neg_x);
-    const svfloat32_t one_plus_exp_neg_x = svadd_f32_x(pg, one, exp_neg_x);
-    return svdiv_f32_x(pg, x, one_plus_exp_neg_x);
-}
-
-#elif defined(__ARM_NEON) && defined(__aarch64__)
+#if defined(__ARM_NEON) && defined(__aarch64__)
 
 // adapted from arm limited optimized routine
 // the maximum error is 1.45358 plus 0.5 ulps
@@ -1269,14 +1030,6 @@ inline static vfloat32m2_t ggml_v_expf_m2(vfloat32m2_t x, int vl) {
         vl);
 }
 
-// computes silu x/(1+exp(-x)) in single precision vector
-inline static vfloat32m2_t ggml_v_silu_m2(vfloat32m2_t x, int vl) {
-    const vfloat32m2_t neg_x = __riscv_vfneg_v_f32m2(x, vl);
-    const vfloat32m2_t exp_neg_x = ggml_v_expf_m2(neg_x, vl);
-    const vfloat32m2_t one_plus_exp_neg_x = __riscv_vfadd_vf_f32m2(exp_neg_x, 1.0f, vl);
-    return __riscv_vfdiv_vv_f32m2(x, one_plus_exp_neg_x, vl);
-}
-
 #endif // __ARM_NEON / __AVX2__ / __SSE2__ / __riscv_v_intrinsic
 
 inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) {

ggml/src/ggml-cuda/CMakeLists.txt

@@ -25,14 +25,10 @@ if (CUDAToolkit_FOUND)
         if (GGML_NATIVE AND CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.6" AND CMAKE_VERSION VERSION_GREATER_EQUAL "3.24")
             set(CMAKE_CUDA_ARCHITECTURES "native")
         else()
-            if (CUDAToolkit_VERSION VERSION_LESS "13")
-                list(APPEND CMAKE_CUDA_ARCHITECTURES 50-virtual 61-virtual 70-virtual)
-            endif ()
-
-            list(APPEND CMAKE_CUDA_ARCHITECTURES 75-virtual 80-virtual 86-real)
-
             if (CUDAToolkit_VERSION VERSION_GREATER_EQUAL "11.8")
-                list(APPEND CMAKE_CUDA_ARCHITECTURES 89-real)
+                set(CMAKE_CUDA_ARCHITECTURES "50-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real;89-real")
+            else()
+                set(CMAKE_CUDA_ARCHITECTURES "50-virtual;61-virtual;70-virtual;75-virtual;80-virtual;86-real")
             endif()
         endif()
     endif()
@@ -48,8 +44,6 @@ if (CUDAToolkit_FOUND)
     list(APPEND GGML_SOURCES_CUDA ${SRCS})
     file(GLOB   SRCS "template-instances/mmq*.cu")
     list(APPEND GGML_SOURCES_CUDA ${SRCS})
-    file(GLOB   SRCS "template-instances/mmf*.cu")
-    list(APPEND GGML_SOURCES_CUDA ${SRCS})
 
     if (GGML_CUDA_FA_ALL_QUANTS)
         file(GLOB   SRCS "template-instances/fattn-vec*.cu")

ggml/src/ggml-cuda/binbcast.cu

@@ -1,6 +1,5 @@
 #include "binbcast.cuh"
 #include <cstdint>
-#include <utility>
 
 static __device__ __forceinline__ float op_repeat(const float a, const float b) {
     return b;
@@ -23,295 +22,73 @@ static __device__ __forceinline__ float op_div(const float a, const float b) {
     return a / b;
 }
 
-template <float (*bin_op)(const float, const float),
-          typename src0_t,
-          typename src1_t,
-          typename dst_t,
-          typename... src1_ptrs>
-static __global__ void k_bin_bcast(const src0_t *         src0,
-                                   const src1_t *         src1,
-                                   dst_t *                dst,
-                                   const int              ne0,
-                                   const int              ne1,
-                                   const int              ne2,
-                                   const uint3            ne3,
-                                   const uint3            ne10,
-                                   const uint3            ne11,
-                                   const uint3            ne12,
-                                   const uint3            ne13,
-                                   /*int s0, */ const int s1,
-                                   const int              s2,
-                                   const int              s3,
-                                   /*int s00,*/ const int s01,
-                                   const int              s02,
-                                   const int              s03,
-                                   /*int s10,*/ const int s11,
-                                   const int              s12,
-                                   const int              s13,
-                                   src1_ptrs... src1s) {
-    const uint32_t i0s = blockDim.x * blockIdx.x + threadIdx.x;
-    const uint32_t i1  = (blockDim.y * blockIdx.y + threadIdx.y);
-    const uint32_t i2  = fastdiv((blockDim.z * blockIdx.z + threadIdx.z), ne3);
-    const uint32_t i3  = (blockDim.z * blockIdx.z + threadIdx.z) - (i2 * ne3.z);
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static __global__ void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s00,*/ int s01, int s02, int s03,
+        /*int s10,*/ int s11, int s12, int s13) {
+    const int i0s = blockDim.x*blockIdx.x + threadIdx.x;
+    const int i1 = (blockDim.y*blockIdx.y + threadIdx.y);
+    const int i2 = (blockDim.z*blockIdx.z + threadIdx.z) / ne3;
+    const int i3 = (blockDim.z*blockIdx.z + threadIdx.z) % ne3;
 
-    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3.z) {
+    if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
         return;
     }
 
-    const uint32_t i11 = fastmodulo(i1, ne11);
-    const uint32_t i12 = fastmodulo(i2, ne12);
-    const uint32_t i13 = fastmodulo(i3, ne13);
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
 
     const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
     const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
     const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
 
-    const src0_t * src0_row = src0 ? (src0 + i_src0) : nullptr;
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
     dst_t * dst_row = dst + i_dst;
 
-    for (int i0 = i0s; i0 < ne0; i0 += blockDim.x * gridDim.x) {
-        const uint32_t i10 = fastmodulo(i0, ne10);
-
-        float result = src0_row ? (float) src0_row[i0] : 0.0f;
-        if constexpr (sizeof...(src1_ptrs) > 0) {
-            result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
-        } else {
-            result = bin_op(result, (float)src1[i_src1 + i10]);
-        }
-
-        dst_row[i0] = (dst_t) result;
+    for (int i0 = i0s; i0 < ne0; i0 += blockDim.x*gridDim.x) {
+        const int i10 = i0 % ne10;
+        dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
     }
 }
 
-template <float (*bin_op)(const float, const float),
-          typename src0_t,
-          typename src1_t,
-          typename dst_t,
-          typename... src1_ptrs>
-static __global__ void k_bin_bcast_unravel(const src0_t *         src0,
-                                           const src1_t *         src1,
-                                           dst_t *                dst,
-                                           const uint3            ne0,
-                                           const uint3            ne1,
-                                           const uint3            ne2,
-                                           const uint32_t         ne3,
-                                           const uint3            prod_012,
-                                           const uint3            prod_01,
-                                           const uint3            ne10,
-                                           const uint3            ne11,
-                                           const uint3            ne12,
-                                           const uint3            ne13,
-                                           /*int s0, */ const int s1,
-                                           const int              s2,
-                                           const int              s3,
-                                           /*int s00,*/ const int s01,
-                                           const int              s02,
-                                           const int              s03,
-                                           /*int s10,*/ const int s11,
-                                           const int              s12,
-                                           const int              s13,
-                                           src1_ptrs... src1s) {
+template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
+static __global__ void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst,
+        int ne0, int ne1, int ne2, int ne3,
+        int ne10, int ne11, int ne12, int ne13,
+        /*int s0, */ int s1,  int s2,  int s3,
+        /*int s00,*/ int s01, int s02, int s03,
+        /*int s10,*/ int s11, int s12, int s13) {
+
     const int i = blockDim.x*blockIdx.x + threadIdx.x;
 
-    const uint32_t i3 = fastdiv(i, prod_012);
-    const uint32_t i2 = fastdiv(i - i3 * prod_012.z, prod_01);
-    const uint32_t i1 = fastdiv(i - i3 * prod_012.z - i2 * prod_01.z, ne0);
-    const uint32_t i0 = i - i3 * prod_012.z - i2 * prod_01.z - i1 * ne0.z;
+    const int i3 = i/(ne2*ne1*ne0);
+    const int i2 = (i/(ne1*ne0)) % ne2;
+    const int i1 = (i/ne0) % ne1;
+    const int i0 = i % ne0;
 
-    if (i0 >= ne0.z || i1 >= ne1.z || i2 >= ne2.z || i3 >= ne3) {
+    if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
         return;
     }
 
-    const int i11 = fastmodulo(i1, ne11);
-    const int i12 = fastmodulo(i2, ne12);
-    const int i13 = fastmodulo(i3, ne13);
+    const int i11 = i1 % ne11;
+    const int i12 = i2 % ne12;
+    const int i13 = i3 % ne13;
 
     const size_t i_src0 =  i3*s03 +  i2*s02 +  i1*s01;
     const size_t i_src1 = i13*s13 + i12*s12 + i11*s11;
     const size_t i_dst  =  i3*s3  +  i2*s2  +  i1*s1;
 
-    const src0_t * src0_row = src0 ? (src0 + i_src0) : nullptr;
+    const src0_t * src0_row = src0 + i_src0;
+    const src1_t * src1_row = src1 + i_src1;
     dst_t * dst_row = dst + i_dst;
 
-    const int i10 = fastmodulo(i0, ne10);
-
-    float result = src0_row ? (float) src0_row[i0] : 0.0f;
-    if constexpr (sizeof...(src1_ptrs) > 0) {
-        result = (..., (result = bin_op(result, (float)src1s[i_src1 + i10])));
-    } else {
-        result = bin_op(result, (float)src1[i_src1 + i10]);
-    }
-
-    dst_row[i0] = (dst_t) result;
-}
-
-template <float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t, size_t... I>
-static void launch_bin_bcast_pack(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
-                                  const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd,
-                                  cudaStream_t stream, std::index_sequence<I...>) {
-    GGML_TENSOR_BINARY_OP_LOCALS
-
-    int nr0 = ne10 / ne0;
-    int nr1 = ne11 / ne1;
-    int nr2 = ne12 / ne2;
-    int nr3 = ne13 / ne3;
-
-    int nr[4] = { nr0, nr1, nr2, nr3 };
-
-    int64_t cne[]  = { ne0, ne1, ne2, ne3 };
-    int64_t cne0[] = { ne00, ne01, ne02, ne03 };
-    int64_t cne1[] = { ne10, ne11, ne12, ne13 };
-
-    size_t cnb[]  = { nb0, nb1, nb2, nb3 };
-    size_t cnb0[] = { nb00, nb01, nb02, nb03 };
-    size_t cnb1[] = { nb10, nb11, nb12, nb13 };
-
-    auto collapse = [](int64_t cne[]) {
-        cne[0] *= cne[1];
-        cne[1] = cne[2];
-        cne[2] = cne[3];
-        cne[3] = 1;
-    };
-
-    auto collapse_nb = [](size_t cnb[], const int64_t cne[]) {
-        cnb[1] *= cne[1];
-        cnb[2] *= cne[2];
-        cnb[3] *= cne[3];
-    };
-
-    if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
-        for (int i = 0; i < 4; i++) {
-            if (nr[i] != 1) {
-                break;
-            }
-            if (i > 0) {
-                collapse_nb(cnb, cne);
-                collapse_nb(cnb0, cne0);
-                collapse_nb(cnb1, cne1);
-                collapse(cne);
-                collapse(cne0);
-                collapse(cne1);
-            }
-        }
-    }
-
-    {
-        int64_t ne0 = cne[0];
-        int64_t ne1 = cne[1];
-        int64_t ne2 = cne[2];
-        int64_t ne3 = cne[3];
-
-        //int64_t ne00 = cne0[0]; GGML_UNUSED(ne00);
-        //int64_t ne01 = cne0[1]; GGML_UNUSED(ne01);
-        //int64_t ne02 = cne0[2]; GGML_UNUSED(ne02);
-        //int64_t ne03 = cne0[3]; GGML_UNUSED(ne03);
-
-        size_t nb0 = cnb[0];
-        size_t nb1 = cnb[1];
-        size_t nb2 = cnb[2];
-        size_t nb3 = cnb[3];
-
-        size_t nb00 = cnb0[0];
-        size_t nb01 = cnb0[1];
-        size_t nb02 = cnb0[2];
-        size_t nb03 = cnb0[3];
-
-        size_t nb10 = cnb1[0];
-        size_t nb11 = cnb1[1];
-        size_t nb12 = cnb1[2];
-        size_t nb13 = cnb1[3];
-
-        size_t s0 = nb0 / sizeof(dst_t);
-        size_t s1 = nb1 / sizeof(dst_t);
-        size_t s2 = nb2 / sizeof(dst_t);
-        size_t s3 = nb3 / sizeof(dst_t);
-
-        size_t s10 = nb10 / sizeof(src1_t);
-        size_t s11 = nb11 / sizeof(src1_t);
-        size_t s12 = nb12 / sizeof(src1_t);
-        size_t s13 = nb13 / sizeof(src1_t);
-
-        size_t s00 = nb00 / sizeof(src0_t);
-        size_t s01 = nb01 / sizeof(src0_t);
-        size_t s02 = nb02 / sizeof(src0_t);
-        size_t s03 = nb03 / sizeof(src0_t);
-
-        GGML_ASSERT(nb0 % sizeof(dst_t) == 0);
-        GGML_ASSERT(nb1 % sizeof(dst_t) == 0);
-        GGML_ASSERT(nb2 % sizeof(dst_t) == 0);
-        GGML_ASSERT(nb3 % sizeof(dst_t) == 0);
-
-        GGML_ASSERT(nb00 % sizeof(src0_t) == 0);
-        GGML_ASSERT(nb01 % sizeof(src0_t) == 0);
-        GGML_ASSERT(nb02 % sizeof(src0_t) == 0);
-        GGML_ASSERT(nb03 % sizeof(src0_t) == 0);
-
-        GGML_ASSERT(nb10 % sizeof(src1_t) == 0);
-        GGML_ASSERT(nb11 % sizeof(src1_t) == 0);
-        GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
-        GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
-
-        GGML_ASSERT(s0 == 1);
-        GGML_ASSERT(s00 == 1);
-        GGML_ASSERT(s10 == 1);
-
-        const int block_size = 128;
-
-        int64_t hne0 = std::max(ne0 / 2LL, 1LL);
-
-        dim3 block_dims;
-        block_dims.x = std::min<unsigned int>(hne0, block_size);
-        block_dims.y = std::min<unsigned int>(ne1, block_size / block_dims.x);
-        block_dims.z = std::min(std::min<unsigned int>(ne2 * ne3, block_size / block_dims.x / block_dims.y), 64U);
-
-        dim3 block_nums((hne0 + block_dims.x - 1) / block_dims.x, (ne1 + block_dims.y - 1) / block_dims.y,
-                        (ne2 * ne3 + block_dims.z - 1) / block_dims.z);
-
-        const uint3 ne10 = init_fastdiv_values((uint32_t) cne1[0]);
-        const uint3 ne11 = init_fastdiv_values((uint32_t) cne1[1]);
-        const uint3 ne12 = init_fastdiv_values((uint32_t) cne1[2]);
-        const uint3 ne13 = init_fastdiv_values((uint32_t) cne1[3]);
-
-        if (block_nums.z > 65535) {
-            int         block_num  = (ne0 * ne1 * ne2 * ne3 + block_size - 1) / block_size;
-            const uint3 prod_012    = init_fastdiv_values((uint32_t) (ne0 * ne1 * ne2));
-            const uint3 prod_01     = init_fastdiv_values((uint32_t) (ne0 * ne1));
-            const uint3 ne0_fastdiv = init_fastdiv_values((uint32_t) ne0);
-            const uint3 ne1_fastdiv = init_fastdiv_values((uint32_t) ne1);
-            const uint3 ne2_fastdiv = init_fastdiv_values((uint32_t) ne2);
-
-            if constexpr (sizeof...(I) > 0) {
-                k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t><<<block_num, block_size, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd, ne0_fastdiv, ne1_fastdiv, ne2_fastdiv, ne3, prod_012, prod_01, ne10, ne11,
-                    ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
-            } else {
-                k_bin_bcast_unravel<bin_op, src0_t, src1_t, dst_t>
-                    <<<block_num, block_size, 0, stream>>>(src0_dd, src1_dd, dst_dd, ne0_fastdiv, ne1_fastdiv,
-                                                           ne2_fastdiv, ne3, prod_012, prod_01, ne10, ne11, ne12, ne13,
-                                                           /* s0, */ s1, s2, s3,
-                                                           /* s00,*/ s01, s02, s03,
-                                                           /* s10,*/ s11, s12, s13);
-            }
-        } else {
-            const uint3 ne3_fastdiv = init_fastdiv_values((uint32_t) ne3);
-            if constexpr (sizeof...(I) > 0) {
-                k_bin_bcast<bin_op, src0_t, src1_t, dst_t><<<block_nums, block_dims, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3_fastdiv, ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12, s13, (const src1_t *) dst->src[I + 1]->data...);
-            } else {
-                k_bin_bcast<bin_op, src0_t, src1_t, dst_t><<<block_nums, block_dims, 0, stream>>>(
-                    src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3_fastdiv, ne10, ne11, ne12, ne13,
-                    /* s0, */ s1, s2, s3,
-                    /* s00,*/ s01, s02, s03,
-                    /* s10,*/ s11, s12, s13);
-            }
-        }
-    }
+    const int i10 = i0 % ne10;
+    dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]);
 }
 
 template <typename T>
@@ -343,14 +120,160 @@ static __global__ void k_repeat_back(
     dst[tid3*ne2*ne1*ne0 + tid2*ne1*ne0 + tid1*ne0 + tid0] = sum;
 }
 
-template <float (*bin_op)(const float, const float), int n_fuse = 1>
+template<float (*bin_op)(const float, const float)>
 struct bin_bcast_cuda {
     template<typename src0_t, typename src1_t, typename dst_t>
     void operator()(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst,
             const src0_t * src0_dd, const src1_t * src1_dd, dst_t * dst_dd,
             cudaStream_t stream) {
-        launch_bin_bcast_pack<bin_op, src0_t, src1_t, dst_t>(
-            src0, src1, dst, src0_dd, src1_dd, dst_dd, stream, std::make_index_sequence<n_fuse>{});
+
+        GGML_TENSOR_BINARY_OP_LOCALS
+
+        int nr0 = ne10/ne0;
+        int nr1 = ne11/ne1;
+        int nr2 = ne12/ne2;
+        int nr3 = ne13/ne3;
+
+        int nr[4] = { nr0, nr1, nr2, nr3 };
+
+        // collapse dimensions until first broadcast dimension
+        int64_t cne[] = {ne0, ne1, ne2, ne3};
+        int64_t cne0[] = {ne00, ne01, ne02, ne03};
+        int64_t cne1[] = {ne10, ne11, ne12, ne13};
+
+        size_t cnb[] = {nb0, nb1, nb2, nb3};
+        size_t cnb0[] = {nb00, nb01, nb02, nb03};
+        size_t cnb1[] = {nb10, nb11, nb12, nb13};
+
+        auto collapse = [](int64_t cne[]) {
+            cne[0] *= cne[1];
+            cne[1] = cne[2];
+            cne[2] = cne[3];
+            cne[3] = 1;
+        };
+
+        auto collapse_nb = [](size_t cnb[], const int64_t cne[]) {
+            cnb[1] *= cne[1];
+            cnb[2] *= cne[2];
+            cnb[3] *= cne[3];
+        };
+
+        if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
+            for (int i = 0; i < 4; i++) {
+                if (nr[i] != 1) {
+                    break;
+                }
+                if (i > 0) {
+                    collapse_nb(cnb, cne);
+                    collapse_nb(cnb0, cne0);
+                    collapse_nb(cnb1, cne1);
+                    collapse(cne);
+                    collapse(cne0);
+                    collapse(cne1);
+                }
+            }
+        }
+
+        {
+            int64_t ne0 = cne[0];
+            int64_t ne1 = cne[1];
+            int64_t ne2 = cne[2];
+            int64_t ne3 = cne[3];
+
+            //int64_t ne00 = cne0[0]; GGML_UNUSED(ne00);
+            //int64_t ne01 = cne0[1]; GGML_UNUSED(ne01);
+            //int64_t ne02 = cne0[2]; GGML_UNUSED(ne02);
+            //int64_t ne03 = cne0[3]; GGML_UNUSED(ne03);
+
+            int64_t ne10 = cne1[0];
+            int64_t ne11 = cne1[1];
+            int64_t ne12 = cne1[2];
+            int64_t ne13 = cne1[3];
+
+            size_t nb0 = cnb[0];
+            size_t nb1 = cnb[1];
+            size_t nb2 = cnb[2];
+            size_t nb3 = cnb[3];
+
+            size_t nb00 = cnb0[0];
+            size_t nb01 = cnb0[1];
+            size_t nb02 = cnb0[2];
+            size_t nb03 = cnb0[3];
+
+            size_t nb10 = cnb1[0];
+            size_t nb11 = cnb1[1];
+            size_t nb12 = cnb1[2];
+            size_t nb13 = cnb1[3];
+
+            size_t s0 = nb0 / sizeof(dst_t);
+            size_t s1 = nb1 / sizeof(dst_t);
+            size_t s2 = nb2 / sizeof(dst_t);
+            size_t s3 = nb3 / sizeof(dst_t);
+
+            size_t s10 = nb10 / sizeof(src1_t);
+            size_t s11 = nb11 / sizeof(src1_t);
+            size_t s12 = nb12 / sizeof(src1_t);
+            size_t s13 = nb13 / sizeof(src1_t);
+
+            size_t s00 = nb00 / sizeof(src0_t);
+            size_t s01 = nb01 / sizeof(src0_t);
+            size_t s02 = nb02 / sizeof(src0_t);
+            size_t s03 = nb03 / sizeof(src0_t);
+
+            GGML_ASSERT(nb0 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb1 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb2 % sizeof(dst_t) == 0);
+            GGML_ASSERT(nb3 % sizeof(dst_t) == 0);
+
+            GGML_ASSERT(nb00 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb01 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb02 % sizeof(src0_t) == 0);
+            GGML_ASSERT(nb03 % sizeof(src0_t) == 0);
+
+            GGML_ASSERT(nb10 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb11 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb12 % sizeof(src1_t) == 0);
+            GGML_ASSERT(nb13 % sizeof(src1_t) == 0);
+
+            GGML_ASSERT(s0 == 1);
+            GGML_ASSERT(s00 == 1);
+            GGML_ASSERT(s10 == 1);
+
+            const int block_size = 128;
+
+            int64_t hne0 = std::max(ne0/2LL, 1LL);
+
+            dim3 block_dims;
+            block_dims.x = std::min<unsigned int>(hne0, block_size);
+            block_dims.y = std::min<unsigned int>(ne1, block_size / block_dims.x);
+            block_dims.z = std::min(std::min<unsigned int>(ne2*ne3, block_size / block_dims.x / block_dims.y), 64U);
+
+            dim3 block_nums(
+                (hne0 + block_dims.x - 1) / block_dims.x,
+                (ne1 + block_dims.y - 1) / block_dims.y,
+                (ne2*ne3 + block_dims.z - 1) / block_dims.z
+            );
+
+            if (block_nums.z > 65535) {
+                // this is the maximum number of blocks in z dimension, fallback to 1D grid kernel
+                int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size;
+                k_bin_bcast_unravel<bin_op><<<block_num, block_size, 0, stream>>>(
+                    src0_dd, src1_dd, dst_dd,
+                    ne0, ne1, ne2, ne3,
+                    ne10, ne11, ne12, ne13,
+                    /* s0, */ s1, s2, s3,
+                    /* s00, */ s01, s02, s03,
+                    /* s10, */ s11, s12, s13);
+            } else {
+                k_bin_bcast<bin_op><<<block_nums, block_dims, 0, stream>>>(
+                    src0_dd, src1_dd, dst_dd,
+                    ne0, ne1, ne2, ne3,
+                    ne10, ne11, ne12, ne13,
+                    /* s0, */ s1, s2, s3,
+                    /* s00, */ s01, s02, s03,
+                    /* s10, */ s11, s12, s13);
+            }
+        }
     }
 };
 
@@ -389,7 +312,7 @@ static void ggml_cuda_op_bin_bcast(
 }
 
 void ggml_cuda_op_repeat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_repeat, 0>>(dst, dst->src[0], dst, nullptr, dst->src[0]->data, dst->data, ctx.stream());
+    ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_repeat>>(dst, dst->src[0], dst, nullptr, dst->src[0]->data, dst->data, ctx.stream());
 }
 
 void ggml_cuda_op_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -408,68 +331,6 @@ void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     ggml_cuda_op_bin_bcast<bin_bcast_cuda<op_div>>(dst->src[0], dst->src[1], dst, dst->src[0]->data, dst->src[1]->data, dst->data, ctx.stream());
 }
 
-template <float (*op)(const float, const float), int n_fuse>
-static void ggml_cuda_op_fused_binbcast_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    cudaStream_t stream = ctx.stream();
-
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-
-    if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
-        launch_bin_bcast_pack<op, float, float, float>(src0, src1, dst,
-            (const float *) src0->data, (const float *) src1->data, (float *) dst->data,
-            stream, std::make_index_sequence<n_fuse>{});
-    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
-        launch_bin_bcast_pack<op, half, half, half>(src0, src1, dst,
-            (const half *) src0->data, (const half *) src1->data, (half *) dst->data,
-            stream, std::make_index_sequence<n_fuse>{});
-    } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
-        launch_bin_bcast_pack<op, half, float, half>(src0, src1, dst,
-            (const half *) src0->data, (const float *) src1->data, (half *) dst->data,
-            stream, std::make_index_sequence<n_fuse>{});
-    } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
-        launch_bin_bcast_pack<op, half, float, float>(src0, src1, dst,
-            (const half *) src0->data, (const float *) src1->data, (float *) dst->data,
-            stream, std::make_index_sequence<n_fuse>{});
-    } else {
-        fprintf(stderr,
-                "%s: unsupported types for fusion: dst: %s, src0: %s, src1: %s\n",
-                __func__, ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
-        GGML_ABORT("fatal error");
-    }
-}
-
-
-void ggml_cuda_op_fused_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst, int n_fuse) {
-    GGML_ASSERT(2 <= n_fuse && n_fuse <= 8);
-
-    switch (n_fuse) {
-        case 2:
-            ggml_cuda_op_fused_binbcast_impl<op_add, 2>(ctx, dst);
-            break;
-        case 3:
-            ggml_cuda_op_fused_binbcast_impl<op_add, 3>(ctx, dst);
-            break;
-        case 4:
-            ggml_cuda_op_fused_binbcast_impl<op_add, 4>(ctx, dst);
-            break;
-        case 5:
-            ggml_cuda_op_fused_binbcast_impl<op_add, 5>(ctx, dst);
-            break;
-        case 6:
-            ggml_cuda_op_fused_binbcast_impl<op_add, 6>(ctx, dst);
-            break;
-        case 7:
-            ggml_cuda_op_fused_binbcast_impl<op_add, 7>(ctx, dst);
-            break;
-        case 8:
-            ggml_cuda_op_fused_binbcast_impl<op_add, 8>(ctx, dst);
-            break;
-        default:
-            GGML_ASSERT(false && "Unsupported n_fuse value");
-    }
-}
-
 void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
 

ggml/src/ggml-cuda/binbcast.cuh

@@ -7,5 +7,3 @@ void ggml_cuda_op_mul(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 void ggml_cuda_op_div(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_repeat_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
-
-void ggml_cuda_op_fused_add(ggml_backend_cuda_context & ctx, ggml_tensor * dst, int n_fuse);

ggml/src/ggml-cuda/common.cuh

@@ -75,8 +75,6 @@
 #define GGML_CUDA_CC_IS_RDNA4(cc) (cc >= GGML_CUDA_CC_RDNA4)
 #define GGML_CUDA_CC_IS_GCN(cc)   (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1)
 #define GGML_CUDA_CC_IS_CDNA(cc)  (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1)
-#define GGML_CUDA_CC_IS_CDNA1(cc) (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_CDNA2)
-#define GGML_CUDA_CC_IS_CDNA2(cc) (cc >= GGML_CUDA_CC_CDNA2 && cc < GGML_CUDA_CC_CDNA3)
 #define GGML_CUDA_CC_IS_CDNA3(cc) (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1)
 
 // Moore Threads
@@ -327,20 +325,6 @@ static constexpr __device__ int ggml_cuda_get_physical_warp_size() {
 #endif // defined(GGML_USE_HIP) && (defined(__GFX9__) || defined(__GFX8__))
 }
 
-// Maximum number of bytes that can be copied in a single instruction.
-static constexpr __device__ int ggml_cuda_get_max_cpy_bytes() {
-#ifdef GGML_USE_HIP
-    return 16;
-#else
-#if __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
-    return 16;
-#else
-    return 8;
-#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
-#endif // GGML_USE_HIP
-}
-
-
 [[noreturn]]
 static __device__ void no_device_code(
     const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) {
@@ -561,45 +545,6 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
 #endif // defined(GGML_USE_HIP)
 }
 
-static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const float v, const float u) {
-    acc += v*u;
-}
-
-static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const float2 v, const float2 u) {
-    acc += v.x*u.x;
-    acc += v.y*u.y;
-}
-
-static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const half2 v, const half2 u) {
-#if defined(GGML_USE_HIP) && (defined(RDNA2) || defined(RDNA3) || defined(RDNA4) || defined(__gfx906__) || defined(CDNA))
-    asm volatile("v_dot2_f32_f16 %0, %1, %2, %0" : "+v"(acc) : "v"(v), "v"(u));
-#else
-#ifdef FAST_FP16_AVAILABLE
-    const float2 tmp = __half22float2(v*u);
-    acc += tmp.x + tmp.y;
-#else
-    const float2 tmpv = __half22float2(v);
-    const float2 tmpu = __half22float2(u);
-    acc += tmpv.x * tmpu.x;
-    acc += tmpv.y * tmpu.y;
-#endif // FAST_FP16_AVAILABLE
-#endif // defined(GGML_USE_HIP) && (defined(RDNA2)  || defined(RDNA3) || defined(RDNA4) || defined(GCN5) || defined(CDNA))
-}
-
-// Aligned memory transfers of 8/16 bytes can be faster than 2 transfers with 4 bytes, especially on AMD.
-template <int nbytes>
-static __device__ __forceinline__ void ggml_cuda_memcpy_1(void * __restrict__ dst, const void * __restrict__ src) {
-    if constexpr (nbytes == 4) {
-        *(int *) dst = *(const int *) src;
-    } else if constexpr (nbytes == 8) {
-        *(int2 *) dst = *(const int2 *) src;
-    } else if constexpr (nbytes == 16) {
-        *(int4 *) dst = *(const int4 *) src;
-    } else {
-        static_assert(nbytes == 0 && nbytes == -1, "bad nbytes");
-    }
-}
-
 static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
 #if CUDART_VERSION >= 12080
     const nv_bfloat16 e = __nv_cvt_e8m0_to_bf16raw(x);
@@ -618,48 +563,6 @@ static __device__ __forceinline__ float ggml_cuda_e8m0_to_fp32(uint8_t x) {
 #endif // CUDART_VERSION >= 12050
 }
 
-// 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;
-static const uint3 init_fastdiv_values(uint32_t d) {
-    GGML_ASSERT(d != 0);
-
-    // compute L = ceil(log2(d));
-    uint32_t L = 0;
-    while (L < 32 && (uint32_t{ 1 } << L) < d) {
-        L++;
-    }
-
-    uint32_t mp = (uint32_t) ((uint64_t{ 1 } << 32) * ((uint64_t{ 1 } << L) - d) / d + 1);
-    // pack divisor as well to reduce error surface
-    return make_uint3(mp, L, d);
-}
-
-static __device__ __forceinline__ uint32_t fastdiv(uint32_t n, const uint3 fastdiv_values) {
-    // expects fastdiv_values to contain <mp, L, divisor> in <x, y, z>
-    // fastdiv_values.z is unused and optimized away by the compiler.
-    // Compute high 32 bits of n * mp
-    const uint32_t hi = __umulhi(n, fastdiv_values.x);
-    // add n, apply bit shift
-    return (hi + n) >> fastdiv_values.y;
-}
-
-static __device__ __forceinline__ uint32_t fastmodulo(uint32_t n, const uint3 fastdiv_values) {
-    // expects  fastdiv_values to contain <mp, L, divisor> in <x, y, z> (see init_fastdiv_values)
-    return n - fastdiv(n, fastdiv_values) * fastdiv_values.z;
-}
-
-// Calculate both division and modulo at once, returns <n/divisor, n%divisor>
-static __device__ __forceinline__ uint2 fast_div_modulo(uint32_t n, const uint3 fastdiv_values) {
-    // expects  fastdiv_values to contain <mp, L, divisor> in <x, y, z> (see init_fastdiv_values)
-    const uint32_t div_val = fastdiv(n, fastdiv_values);
-    const uint32_t mod_val = n - div_val * fastdiv_values.z;
-    return make_uint2(div_val, mod_val);
-}
-
 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, float2 & v);
 
 static __device__ __forceinline__ float get_alibi_slope(

ggml/src/ggml-cuda/conv2d.cu

@@ -1,166 +0,0 @@
-#include "conv2d.cuh"
-#include "convert.cuh"
-
-struct conv_params {
-    const int64_t IW, IH;
-    const int64_t OW, OH;
-    const int64_t KW, KH;
-    const int64_t ST_X, ST_Y;
-    const int64_t PD_X, PD_Y;
-    const int64_t DL_X, DL_Y;
-    const int64_t IC, OC;
-    const int64_t B;
-    const int64_t TOTAL;
-};
-
-struct kernel_bounds {
-    int64_t y_min, y_max;
-    int64_t x_min, x_max;
-};
-
-__device__ __forceinline__ int64_t max64(int64_t a, int64_t b) {
-    return (a > b) ? a : b;
-}
-
-__device__ __forceinline__ int64_t min64(int64_t a, int64_t b) {
-    return (a < b) ? a : b;
-}
-
-__device__ __forceinline__ kernel_bounds calculate_kernel_bounds(int64_t out_x, int64_t out_y, const conv_params & P) {
-    kernel_bounds bounds;
-    bounds.y_min = max64(0, (P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
-    bounds.y_max = min64(P.KH, (P.IH + P.PD_Y - out_y * P.ST_Y + P.DL_Y - 1) / P.DL_Y);
-    bounds.x_min = max64(0, (P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
-    bounds.x_max = min64(P.KW, (P.IW + P.PD_X - out_x * P.ST_X + P.DL_X - 1) / P.DL_X);
-    return bounds;
-}
-
-__device__ __forceinline__ int calculate_input_coord(int64_t out_coord,
-                                                     int64_t kern_coord,
-                                                     int64_t stride,
-                                                     int64_t dilation,
-                                                     int64_t padding) {
-    return out_coord * stride + kern_coord * dilation - padding;
-}
-
-struct whcn_layout {
-    __device__ static int64_t input_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv_params & P) {
-        return n * (P.IC * P.IW * P.IH) + c * P.IW * P.IH + y * P.IW + x;
-    }
-
-    __device__ static int64_t kernel_index(int64_t c_out, int64_t c_in, int64_t ky, int64_t kx, const conv_params & P) {
-        return c_out * (P.IC * P.KH * P.KW) + c_in * (P.KH * P.KW) + ky * P.KW + kx;
-    }
-
-    __device__ static int64_t output_index(int64_t n, int64_t c, int64_t y, int64_t x, const conv_params & P) {
-        return n * (P.OC * P.OW * P.OH) + c * P.OW * P.OH + y * P.OW + x;
-    }
-
-    __device__ static void unpack_indices(int64_t             global_idx,
-                                          const conv_params & P,
-                                          int64_t &           n,
-                                          int64_t &           c,
-                                          int64_t &           out_y,
-                                          int64_t &           out_x) {
-        out_x = global_idx % P.OW;
-        out_y = (global_idx / P.OW) % P.OH;
-        c     = (global_idx / (P.OW * P.OH)) % P.OC;
-        n     = global_idx / (P.OW * P.OH * P.OC);
-    }
-};
-
-template <typename T, typename Layout>
-static __global__ void conv2d_kernel(const float * __restrict__ input,
-                                     const T * __restrict__ kernel,
-                                     float * __restrict__ output,
-                                     const conv_params P) {
-    const int64_t global_idx = blockIdx.x * blockDim.x + threadIdx.x;
-
-    if (global_idx >= P.TOTAL) {
-        return;
-    }
-
-    int64_t n, c_out, out_y, out_x;
-    Layout::unpack_indices(global_idx, P, n, c_out, out_y, out_x);
-
-    float acc = 0.0f;
-
-    for (int64_t c_in = 0; c_in < P.IC; ++c_in) {
-        kernel_bounds bounds = calculate_kernel_bounds(out_x, out_y, P);
-
-        for (int64_t ky = bounds.y_min; ky < bounds.y_max; ++ky) {
-            const int64_t in_y = calculate_input_coord(out_y, ky, P.ST_Y, P.DL_Y, P.PD_Y);
-
-            for (int64_t kx = bounds.x_min; kx < bounds.x_max; ++kx) {
-                const int64_t in_x = calculate_input_coord(out_x, kx, P.ST_X, P.DL_X, P.PD_X);
-
-                const float input_val = input[Layout::input_index(n, c_in, in_y, in_x, P)];
-                const T kernel_val = kernel[Layout::kernel_index(c_out, c_in, ky, kx, P)];
-                acc += (input_val * ggml_cuda_cast<float>(kernel_val));
-            }
-        }
-    }
-
-    // [N, OC, OH, OW]
-    output[Layout::output_index(n, c_out, out_y, out_x, P)] = acc;
-}
-
-template <typename T>
-static void conv2d_cuda(const float * X_D, const T * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
-    const int blocks = (P.TOTAL + CUDA_CONV2D_BLOCK_SIZE - 1) / CUDA_CONV2D_BLOCK_SIZE;
-    conv2d_kernel<T, whcn_layout><<<blocks, CUDA_CONV2D_BLOCK_SIZE, 0, st>>>(X_D, K_D, Y_D, P);
-}
-
-static void conv2d_cuda_f16(const float * X_D, const half * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
-    conv2d_cuda<half>(X_D, K_D, Y_D, P, st);
-}
-
-static void conv2d_cuda_f32(const float * X_D, const float * K_D, float * Y_D, const conv_params P, cudaStream_t st) {
-    conv2d_cuda<float>(X_D, K_D, Y_D, P, st);
-}
-
-void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * kernel = dst->src[0];
-    const ggml_tensor * input  = dst->src[1];
-    float *             K_D    = (float *) kernel->data;
-    const float *       X_D    = (const float *) input->data;
-    float *             Y_D    = (float *) dst->data;
-
-    GGML_ASSERT(ggml_is_contiguous(kernel));
-    GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);
-
-    // same number of input channels
-    GGML_ASSERT(input->ne[2] == kernel->ne[2]);
-
-    cudaStream_t st = ctx.stream();
-
-    const int32_t * p    = (const int32_t *) dst->op_params;
-    const int       ST_X = p[0];  // stride_x
-    const int       ST_Y = p[1];  // stride_y
-    const int       PD_X = p[2];  // padding_x
-    const int       PD_Y = p[3];  // padding_y
-    const int       DL_X = p[4];  // dilation_x
-    const int       DL_Y = p[5];  // dilation_y
-
-    // No cwhn
-    GGML_ASSERT(p[6] == false);
-
-    const int IW = input->ne[0];   // input_w
-    const int IH = input->ne[1];   // input_h
-    const int OW = dst->ne[0];     // output_w
-    const int OH = dst->ne[1];     // output_h
-    const int KW = kernel->ne[0];  // kernel_w
-    const int KH = kernel->ne[1];  // kernel_h
-    const int IC = input->ne[2];   // input_channels
-    const int OC = kernel->ne[3];  // ouptut_chanles
-    const int B  = input->ne[3];   // n_batches
-
-    const int64_t total  = B * OC * OH * OW;
-    conv_params   params = { IW, IH, OW, OH, KW, KH, ST_X, ST_Y, PD_X, PD_Y, DL_X, DL_Y, IC, OC, B, total };
-
-    if (kernel->type == GGML_TYPE_F16) {
-        conv2d_cuda_f16(X_D, (half *) K_D, Y_D, params, st);
-    } else {
-        conv2d_cuda_f32(X_D, K_D, Y_D, params, st);
-    }
-}

ggml/src/ggml-cuda/conv2d.cuh

@@ -1,5 +0,0 @@
-#pragma once
-#include "common.cuh"
-
-#define CUDA_CONV2D_BLOCK_SIZE 256
-void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst);

ggml/src/ggml-cuda/convert.cuh

@@ -38,8 +38,6 @@ template<typename dst_t, typename src_t>
         return __float2bfloat16(float(x));
     } else if constexpr(std::is_same_v<src_t, nv_bfloat16>) {
         return __bfloat162float(x);
-    } else if constexpr(std::is_same_v<dst_t, int32_t>) {
-        return int32_t(x);
     } else {
         return float(x);
     }