opencl: optimize mxfp4 kernels (#16037)

- flatten mxfp4 and packed fp4->fp16 bit-wise convert function (replace lut)
- MoE kernel optimizations

---------

Co-authored-by: Li He <lih@qti.qualcomm.com>

.clang-format

@@ -22,6 +22,13 @@ 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
 BinPackParameters: false # OnePerLine
 BitFieldColonSpacing: Both

.devops/rocm.Dockerfile

@@ -4,7 +4,7 @@ ARG UBUNTU_VERSION=24.04
 ARG ROCM_VERSION=6.4
 ARG AMDGPU_VERSION=6.4
 
-# Target the CUDA build image
+# Target the ROCm build image
 ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
 
 ### Build image
@@ -15,16 +15,13 @@ 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-6.2.4/reference/system-requirements.html
+#check https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.1/reference/system-requirements.html
 
-ARG ROCM_DOCKER_ARCH='gfx803,gfx900,gfx906,gfx908,gfx90a,gfx942,gfx1010,gfx1030,gfx1032,gfx1100,gfx1101,gfx1102'
-#ARG ROCM_DOCKER_ARCH=gfx1100
+ARG ROCM_DOCKER_ARCH='gfx803;gfx900;gfx906;gfx908;gfx90a;gfx942;gfx1010;gfx1030;gfx1032;gfx1100;gfx1101;gfx1102;gfx1200;gfx1201;gfx1151'
+#ARG ROCM_DOCKER_ARCH='gfx1151'
 
-# Set nvcc architectured
+# Set ROCm architectures
 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 \
@@ -39,8 +36,16 @@ 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 -DAMDGPU_TARGETS=$ROCM_DOCKER_ARCH -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF \
+    cmake -S . -B build \
+        -DGGML_HIP=ON \
+        -DGGML_HIP_ROCWMMA_FATTN=ON \
+        -DCMAKE_HIP_FLAGS="-I$(pwd)/rocwmma/library/include/" \
+        -DAMDGPU_TARGETS="$ROCM_DOCKER_ARCH" \
+        -DGGML_BACKEND_DL=ON -DGGML_CPU_ALL_VARIANTS=ON \
+        -DCMAKE_BUILD_TYPE=Release -DLLAMA_BUILD_TESTS=OFF \
     && cmake --build build --config Release -j$(nproc)
 
 RUN mkdir -p /app/lib \

.editorconfig

@@ -52,3 +52,11 @@ 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-14
+    runs-on: macos-latest
 
     steps:
       - name: Clone
@@ -88,6 +88,7 @@ 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
@@ -126,7 +127,8 @@ jobs:
             -DCMAKE_BUILD_RPATH="@loader_path" \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DGGML_METAL=OFF \
-            -DGGML_RPC=ON
+            -DGGML_RPC=ON \
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Test
@@ -136,7 +138,7 @@ jobs:
           ctest -L main --verbose --timeout 900
 
   macOS-latest-cmake-arm64-webgpu:
-    runs-on: macos-14
+    runs-on: macos-latest
 
     steps:
       - name: Clone
@@ -709,6 +711,7 @@ jobs:
 
   macOS-latest-swift:
     runs-on: macos-latest
+    needs: ios-xcode-build
 
     strategy:
       matrix:
@@ -725,6 +728,12 @@ 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
@@ -746,11 +755,6 @@ 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
 
@@ -1050,9 +1054,13 @@ 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
@@ -1061,16 +1069,14 @@ jobs:
       - name: Clone rocWMMA repository
         id: clone_rocwmma
         run: |
-          git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1
+          git clone https://github.com/rocm/rocwmma --branch rocm-${{ env.ROCM_VERSION }} --depth 1
 
       - name: Cache ROCm Installation
         id: cache-rocm
         uses: actions/cache@v4
         with:
           path: C:\Program Files\AMD\ROCm
-          key: rocm-6.1-${{ runner.os }}-v1
-          restore-keys: |
-            rocm-6.1-${{ runner.os }}-
+          key: rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
 
       - name: Install ROCm
         if: steps.cache-rocm.outputs.cache-hit != 'true'
@@ -1078,7 +1084,7 @@ jobs:
         run: |
           $ErrorActionPreference = "Stop"
           write-host "Downloading AMD HIP SDK Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-WinSvr2022-For-HIP.exe" -OutFile "${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)
@@ -1166,8 +1172,17 @@ 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 -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 -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
 
   android-build:
     runs-on: ubuntu-latest

.github/workflows/release.yml

@@ -108,7 +108,8 @@ jobs:
             -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON \
             -DLLAMA_FATAL_WARNINGS=ON \
             -DGGML_METAL=OFF \
-            -DGGML_RPC=ON
+            -DGGML_RPC=ON \
+            -DCMAKE_OSX_DEPLOYMENT_TARGET=13.3
           cmake --build build --config Release -j $(sysctl -n hw.logicalcpu)
 
       - name: Determine tag name
@@ -528,11 +529,14 @@ jobs:
   windows-hip:
     runs-on: windows-2022
 
+    env:
+      HIPSDK_INSTALLER_VERSION: "25.Q3"
+
     strategy:
       matrix:
         include:
           - name: "radeon"
-            gpu_targets: "gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
+            gpu_targets: "gfx1151;gfx1200;gfx1201;gfx1100;gfx1101;gfx1102;gfx1030;gfx1031;gfx1032"
 
     steps:
       - name: Clone
@@ -542,21 +546,19 @@ jobs:
       - name: Clone rocWMMA repository
         id: clone_rocwmma
         run: |
-          git clone https://github.com/rocm/rocwmma --branch rocm-6.2.4 --depth 1
+          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-6.1-${{ runner.os }}-v1
-          restore-keys: |
-            rocm-6.1-${{ runner.os }}-
+          key: rocm-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ runner.os }}
 
       - name: ccache
         uses: ggml-org/ccache-action@v1.2.16
         with:
-          key: windows-latest-cmake-hip-${{ matrix.name }}-x64
+          key: windows-latest-cmake-hip-${{ env.HIPSDK_INSTALLER_VERSION }}-${{ matrix.name }}-x64
           evict-old-files: 1d
 
       - name: Install ROCm
@@ -565,7 +567,7 @@ jobs:
         run: |
           $ErrorActionPreference = "Stop"
           write-host "Downloading AMD HIP SDK Installer"
-          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-24.Q3-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe"
+          Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-${{ env.HIPSDK_INSTALLER_VERSION }}-WinSvr2022-For-HIP.exe" -OutFile "${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)
@@ -610,9 +612,12 @@ 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

@@ -76,51 +76,206 @@ jobs:
         run: |
           pip install -r tools/server/tests/requirements.txt
 
-      # Setup nodejs (to be used for verifying bundled index.html)
-      - uses: actions/setup-node@v4
+  webui-setup:
+    name: WebUI Setup
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
         with:
-          node-version: '22.11.0'
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
 
-      - name: WebUI - Install dependencies
-        id: webui_lint
+      - 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
         run: |
-          cd tools/server/webui
-          npm ci
+          sudo apt-get update
+          sudo apt-get -y install \
+            build-essential \
+            xxd \
+            git \
+            cmake \
+            curl \
+            wget \
+            language-pack-en \
+            libcurl4-openssl-dev
 
-      - name: WebUI - Check code format
-        id: webui_format
+      - 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
         run: |
-          git config --global --add safe.directory $(realpath .)
-          cd tools/server/webui
-          git status
+          pip install -r tools/server/tests/requirements.txt
 
-          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: 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"
 
-      - 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: Install WebUI dependencies
+        run: npm ci
+        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 WebUI
+        run: npm run build
+        working-directory: tools/server/webui
 
       - name: Build (no OpenMP)
         id: cmake_build_no_openmp

.gitignore

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

.windsurf/rules/css-architecture.md

@@ -0,0 +1,7 @@
+---
+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

@@ -0,0 +1,48 @@
+---
+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

@@ -0,0 +1,9 @@
+---
+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

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

CMakeLists.txt

@@ -58,6 +58,12 @@ 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
 #
@@ -82,6 +88,7 @@ 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)

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 -DGGML_METAL_USE_BF16=ON"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
 fi
 
 if [ ! -z ${GG_BUILD_CUDA} ]; then
@@ -270,7 +270,9 @@ function gg_run_ctest_with_model_debug {
     local model; model=$(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 ..
 }
@@ -281,7 +283,15 @@ 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 ..
 }
@@ -860,10 +870,7 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
 fi
 
 ret=0
-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_debug
 test $ret -eq 0 && gg_run ctest_release
 
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
@@ -871,9 +878,7 @@ 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
-        if [ -z ${GG_BUILD_SYCL} ]; then
-            test $ret -eq 0 && gg_run test_scripts_debug
-        fi
+        test $ret -eq 0 && gg_run test_scripts_debug
         test $ret -eq 0 && gg_run test_scripts_release
     fi
 
@@ -884,9 +889,7 @@ 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
-        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_debug
         test $ret -eq 0 && gg_run ctest_with_model_release
     fi
 fi

common/arg.cpp

@@ -57,12 +57,32 @@ static std::string read_file(const std::string & fname) {
 }
 
 static void write_file(const std::string & fname, const std::string & content) {
-    std::ofstream file(fname);
+    const std::string fname_tmp = fname + ".tmp";
+    std::ofstream     file(fname_tmp);
     if (!file) {
         throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
     }
-    file << content;
-    file.close();
+
+    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()));
+    }
 }
 
 common_arg & common_arg::set_examples(std::initializer_list<enum llama_example> examples) {
@@ -217,250 +237,294 @@ struct curl_slist_ptr {
     }
 };
 
-#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));
+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__);
     }
 
-    LOG_ERR("%s: curl_easy_perform() failed after %d attempts\n", __func__, max_attempts);
-
-    return false;
+    return res;
 }
 
-// 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
+// Send a HEAD request to retrieve the etag and last-modified headers
+struct common_load_model_from_url_headers {
     std::string etag;
     std::string last_modified;
+    std::string accept_ranges;
+};
 
-    if (file_exists) {
-        if (offline) {
-            LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
-            return true; // skip verification/downloading
+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;
         }
-        // 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());
     }
 
-    // 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;
-    };
+    return n_items;
+}
 
-    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
+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));
+}
 
-    // Initialize libcurl
-    curl_ptr       curl(curl_easy_init(), &curl_easy_cleanup);
-    curl_slist_ptr http_headers;
+// helper function to hide password in URL
+static std::string llama_download_hide_password_in_url(const std::string & url) {
+    // Use regex to match and replace the user[:password]@ pattern in URLs
+    // Pattern: scheme://[user[:password]@]host[...]
+    static const std::regex url_regex(R"(^(?:[A-Za-z][A-Za-z0-9+.-]://)(?:[^/@]+@)?.$)");
+    std::smatch             match;
+
+    if (std::regex_match(url, match, url_regex)) {
+        // match[1] = scheme (e.g., "https://")
+        // match[2] = user[:password]@ part
+        // match[3] = rest of URL (host and path)
+        return match[1].str() + "********@" + match[3].str();
+    }
+
+    return url;  // No credentials found or malformed URL
+}
+
+static void common_curl_easy_setopt_head(CURL * curl, const std::string & url) {
+    // Set the URL, allow to follow http redirection
+    curl_easy_setopt(curl, CURLOPT_URL, url.c_str());
+    curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L);
+
+#    if defined(_WIN32)
+    // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of
+    //   operating system. Currently implemented under MS-Windows.
+    curl_easy_setopt(curl, CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
+#    endif
+
+    curl_easy_setopt(curl, CURLOPT_NOBODY, 1L);      // will trigger the HEAD verb
+    curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 1L);  // hide head request progress
+    curl_easy_setopt(curl, CURLOPT_HEADERFUNCTION, common_header_callback);
+}
+
+static void common_curl_easy_setopt_get(CURL * curl) {
+    curl_easy_setopt(curl, CURLOPT_NOBODY, 0L);
+    curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, common_write_callback);
+
+    //  display download progress
+    curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L);
+}
+
+static bool common_pull_file(CURL * curl, const std::string & path_temporary) {
+    if (std::filesystem::exists(path_temporary)) {
+        const std::string partial_size = std::to_string(std::filesystem::file_size(path_temporary));
+        LOG_INF("%s: server supports range requests, resuming download from byte %s\n", __func__, partial_size.c_str());
+        const std::string range_str = partial_size + "-";
+        curl_easy_setopt(curl, CURLOPT_RANGE, range_str.c_str());
+    }
+
+    // Always open file in append mode could be resuming
+    std::unique_ptr<FILE, FILE_deleter> outfile(fopen(path_temporary.c_str(), "ab"));
+    if (!outfile) {
+        LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path_temporary.c_str());
+        return false;
+    }
+
+    common_curl_easy_setopt_get(curl);
+    curl_easy_setopt(curl, CURLOPT_WRITEDATA, outfile.get());
+
+    return common_curl_perf(curl) == CURLE_OK;
+}
+
+static bool common_download_head(CURL *              curl,
+                                 curl_slist_ptr &    http_headers,
+                                 const std::string & url,
+                                 const std::string & bearer_token) {
     if (!curl) {
         LOG_ERR("%s: error initializing libcurl\n", __func__);
         return false;
     }
 
-    // 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());
-    }
-    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;
+        http_headers.ptr        = curl_slist_append(http_headers.ptr, auth_header.c_str());
     }
 
-    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_setopt(curl, CURLOPT_HTTPHEADER, http_headers.ptr);
+    common_curl_easy_setopt_head(curl, url);
+    return common_curl_perf(curl) == CURLE_OK;
+}
 
-    // 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;
-        }
-    }
+// 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 (should_download) {
-        std::string path_temporary = path + ".downloadInProgress";
+        // Check if the file already exists locally
+        const auto file_exists = std::filesystem::exists(path);
         if (file_exists) {
-            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());
+            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());
         }
 
-        // Set the output file
+        bool head_request_ok = false;
+        bool should_download = !file_exists;  // by default, we should download if the file does not exist
 
-        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");
+        // Initialize libcurl
+        curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
+        common_load_model_from_url_headers headers;
+        curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers);
+        curl_slist_ptr http_headers;
+        const bool     was_perform_successful = common_download_head(curl.get(), http_headers, url, bearer_token);
         if (!was_perform_successful) {
-            return false;
+            head_request_ok = false;
         }
 
         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;
+        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;
         }
 
-        // 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;
+        // 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;
+            }
         }
-    } else {
-        LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
+
+        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;
     }
 
     return true;
@@ -745,6 +809,124 @@ 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
 //
@@ -795,7 +977,9 @@ 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.hf_repo.empty()) {
+        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()) {
             // short-hand to avoid specifying --hf-file -> default it to --model
             if (model.hf_file.empty()) {
                 if (model.path.empty()) {
@@ -1184,7 +1368,7 @@ 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_GPU) {
+            if (!dev || ggml_backend_dev_type(dev) == GGML_BACKEND_DEVICE_TYPE_CPU) {
                 throw std::invalid_argument(string_format("invalid device: %s", device.c_str()));
             }
             devices.push_back(dev);
@@ -1194,7 +1378,7 @@ static std::vector<ggml_backend_dev_t> parse_device_list(const std::string & val
     return devices;
 }
 
-static void add_rpc_devices(std::string servers) {
+static void add_rpc_devices(const std::string & servers) {
     auto rpc_servers = string_split<std::string>(servers, ',');
     if (rpc_servers.empty()) {
         throw std::invalid_argument("no RPC servers specified");
@@ -1584,7 +1768,7 @@ 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}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_DIFFUSION}));
     add_opt(common_arg(
         {"--no-perf"},
         string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
@@ -2396,24 +2580,15 @@ 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> rpc_devices;
-            std::vector<ggml_backend_dev_t> all_devices;
+            std::vector<ggml_backend_dev_t> 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_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);
-                    }
+                if (ggml_backend_dev_type(dev) != GGML_BACKEND_DEVICE_TYPE_CPU) {
+                    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 (size_t i = 0; i < all_devices.size(); ++i) {
-                auto * dev = all_devices[i];
+            for (auto * dev : devices) {
                 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);
@@ -2437,7 +2612,7 @@ 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({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
+            params.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
         }
     ).set_env("LLAMA_ARG_CPU_MOE"));
     add_opt(common_arg(
@@ -2450,7 +2625,7 @@ 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(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
+                buft_overrides.push_back(llm_ffn_exps_block_regex(i));
                 params.tensor_buft_overrides.push_back({buft_overrides.back().c_str(), ggml_backend_cpu_buffer_type()});
             }
         }
@@ -2459,7 +2634,7 @@ 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({"\\.ffn_(up|down|gate)_exps", ggml_backend_cpu_buffer_type()});
+            params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
         }
     ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
     add_opt(common_arg(
@@ -2471,7 +2646,7 @@ 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(string_format("blk\\.%d\\.ffn_(up|down|gate)_exps", i));
+                buft_overrides_draft.push_back(llm_ffn_exps_block_regex(i));
                 params.speculative.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
             }
         }
@@ -2636,6 +2811,15 @@ 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"

common/common.h

@@ -193,10 +193,11 @@ 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 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
 };
 
 struct common_params_speculative {
@@ -287,9 +288,9 @@ 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        = 32.0f; // 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        = -1.0f; // 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
@@ -452,7 +453,7 @@ struct common_params {
 
     std::string slot_save_path;
 
-    float slot_prompt_similarity = 0.5f;
+    float slot_prompt_similarity = 0.1f;
 
     // batched-bench params
     bool is_pp_shared = false;
@@ -733,6 +734,20 @@ 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

@@ -257,12 +257,13 @@ std::unordered_map<std::string, BuiltinRule> STRING_FORMAT_RULES = {
 };
 
 static bool is_reserved_name(const std::string & name) {
-    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);
-    }
+    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;
+    }();
     return RESERVED_NAMES.find(name) != RESERVED_NAMES.end();
 }
 

convert_hf_to_gguf.py

@@ -735,6 +735,9 @@ 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"
@@ -885,6 +888,9 @@ 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")
@@ -2387,7 +2393,10 @@ class SmolVLMModel(MmprojModel):
         return [] # skip other tensors
 
 
-@ModelBase.register("Llama4ForConditionalGeneration")
+@ModelBase.register(
+    "Llama4ForConditionalGeneration",
+    "Llama4ForCausalLM",
+)
 class Llama4Model(LlamaModel):
     model_arch = gguf.MODEL_ARCH.LLAMA4
     undo_permute = False
@@ -2405,6 +2414,10 @@ 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."):
@@ -2682,12 +2695,20 @@ class BitnetModel(TextModel):
         yield (new_name, data_torch)
 
 
-@ModelBase.register("GrokForCausalLM")
+@ModelBase.register("GrokForCausalLM", "Grok1ForCausalLM")
 class GrokModel(TextModel):
     model_arch = gguf.MODEL_ARCH.GROK
 
     def set_vocab(self):
-        self._set_vocab_sentencepiece()
+        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()
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -2695,11 +2716,46 @@ class GrokModel(TextModel):
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
-    _experts: list[dict[str, Tensor]] | None = None
+        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 = ""
 
     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 name.find(".moe.") != -1:
+        if is_expert or self._cur_expert:
             n_experts = self.hparams["num_local_experts"]
 
             assert bid is not None
@@ -2707,32 +2763,41 @@ class GrokModel(TextModel):
             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 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:
+            # 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 []
+            else:
+                self._cur_expert = ""
 
-        return [(self.map_tensor_name(name), data_torch)]
+            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
 
 
 @ModelBase.register("DbrxForCausalLM")
@@ -5951,9 +6016,34 @@ 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):
@@ -8184,6 +8274,76 @@ 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

@@ -139,6 +139,7 @@ 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
@@ -158,6 +159,7 @@ 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"},
 ]
 
 

docs/backend/CANN.md

@@ -318,3 +318,7 @@ Operators are executed using ACL graph execution, rather than in op-by-op (eager
 ### 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

@@ -241,8 +241,8 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 |            | VX/VXE/VXE2 | zDNN | Spyre |
 |------------|-------------|------|-------|
 | FP32       | ✅           | ✅    | ❓     |
-| FP16       | ✅           | ❓    | ❓     |
-| BF16       | 🚫           | ❓    | ❓     |
+| FP16       | ✅           | ✅    | ❓     |
+| BF16       | 🚫           | ✅    | ❓     |
 | Q4_0       | ✅           | ❓    | ❓     |
 | Q4_1       | ✅           | ❓    | ❓     |
 | MXFP4      | 🚫           | ❓    | ❓     |
@@ -272,4 +272,4 @@ IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongl
 -   🚫 - 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 6, 2025.
+Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on Sep 7, 2025.

docs/ops.md

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

examples/diffusion/diffusion-cli.cpp

@@ -510,19 +510,27 @@ static void diffusion_generate(llama_context *          ctx,
     n_generated = params.max_length;
 }
 
-static std::string format_input_text(const std::string & prompt, bool use_chat_template, llama_model * model) {
+static std::string format_input_text(const std::string & prompt, const std::string & system_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              user_msg;
-    user_msg.role                = "user";
-    user_msg.content             = prompt;
-    inputs.add_generation_prompt = true;
+    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);
+    inputs.add_generation_prompt = true;
 
     auto result = common_chat_templates_apply(chat_templates.get(), inputs);
 
@@ -579,7 +587,8 @@ 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.enable_chat_template, model);
+
+    std::string         formatted_prompt = format_input_text(params.prompt, params.system_prompt, params.enable_chat_template, model);
 
     std::vector<llama_token> input_tokens = common_tokenize(vocab,
                                                             formatted_prompt,
@@ -596,6 +605,7 @@ 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/simple/simple.cpp

@@ -145,6 +145,20 @@ 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();

ggml/CMakeLists.txt

@@ -190,7 +190,6 @@ 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})

ggml/include/ggml-backend.h

@@ -132,6 +132,8 @@ 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
     };
@@ -150,11 +152,21 @@ 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;
     };
 

ggml/include/ggml-metal.h

@@ -39,6 +39,7 @@ 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/include/ggml-zdnn.h

@@ -7,8 +7,6 @@
 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)->array[0]; \
+    const type prefix##0 = (pointer) ? (pointer)->array[0] : 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)->array[1]; \
+    const type prefix##1 = (pointer) ? (pointer)->array[1] : 0; \
     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)->array[2]; \
+    const type prefix##2 = (pointer) ? (pointer)->array[2] : 0; \
     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)->array[3]; \
+    const type prefix##3 = (pointer) ? (pointer)->array[3] : 0; \
     GGML_UNUSED(prefix##3);
 
 #define GGML_TENSOR_UNARY_OP_LOCALS \

ggml/src/ggml-backend-impl.h

@@ -8,7 +8,7 @@
 extern "C" {
 #endif
 
-    #define GGML_BACKEND_API_VERSION 1
+    #define GGML_BACKEND_API_VERSION 2
 
     //
     // Backend buffer type
@@ -116,7 +116,7 @@ extern "C" {
         void (*event_wait)  (ggml_backend_t backend, ggml_backend_event_t event);
 
         // (optional) sort/optimize the nodes in the graph
-        void                      (*optimize_graph)    (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+        void                      (*graph_optimize)    (ggml_backend_t backend, struct ggml_cgraph * cgraph);
     };
 
     struct ggml_backend {

ggml/src/ggml-backend-reg.cpp

@@ -400,9 +400,8 @@ 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);
-    if (!dev) {
-        dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
-    }
+    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) {
         return nullptr;
     }

ggml/src/ggml-backend.cpp

@@ -463,10 +463,10 @@ void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event)
     backend->iface.event_wait(backend, event);
 }
 
-static void ggml_backend_optimize_graph(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+static void ggml_backend_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     GGML_ASSERT(backend);
-    if (backend->iface.optimize_graph != NULL) {
-        backend->iface.optimize_graph(backend, cgraph);
+    if (backend->iface.graph_optimize != NULL) {
+        backend->iface.graph_optimize(backend, cgraph);
     }
 }
 
@@ -1307,7 +1307,7 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra
 
         // Optimize this split of the graph. This needs to happen before we make graph_copy,
         // so they are in sync.
-        ggml_backend_optimize_graph(sched->backends[split->backend_id], &split->graph);
+        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++) {

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

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

ggml/src/ggml-cann/common.h

@@ -526,7 +526,10 @@ struct ggml_backend_cann_context {
      */
     aclrtStream stream(int stream) {
         if (streams[stream] == nullptr) {
-            ggml_cann_set_device(device);
+            // 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));
             ACL_CHECK(aclrtCreateStream(&streams[stream]));
         }
         return streams[stream];

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

@@ -75,13 +75,12 @@
  * @param device The device ID to set.
  */
 void ggml_cann_set_device(const int32_t device) {
-    // TODO: uncomment these lines after empty context has fixed.
-    // int current_device;
-    // ACL_CHECK(aclrtGetDevice(&current_device));
+    int current_device = -1;
+    aclrtGetDevice(&current_device);
 
-    // if (device == current_device) {
-    //   return;
-    // }
+    if (device == current_device) {
+      return;
+    }
     ACL_CHECK(aclrtSetDevice(device));
 }
 
@@ -2360,6 +2359,21 @@ static enum ggml_status ggml_backend_cann_graph_compute(
     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;
     }
@@ -2742,7 +2756,7 @@ 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,
-    /* .optimize_graph          = */ NULL,
+    /* .graph_optimize          = */ NULL,
 };
 
 /**

ggml/src/ggml-cpu/CMakeLists.txt

@@ -224,7 +224,13 @@ 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)
-                        message(STATUS "ARM feature ${feature} enabled")
+                        # Special handling for MATMUL_INT8 when machine doesn't support i8mm
+                        if ("${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()
                     endif()
                 endforeach()
             endif()

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

@@ -190,7 +190,7 @@ static const struct ggml_backend_i ggml_backend_cpu_i = {
     /* .graph_compute           = */ ggml_backend_cpu_graph_compute,
     /* .event_record            = */ NULL,
     /* .event_wait              = */ NULL,
-    /* .optimize_graph          = */ NULL,
+    /* .graph_optimize          = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_cpu_guid(void) {

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

@@ -515,9 +515,6 @@ 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;
             }

ggml/src/ggml-cpu/ops.cpp

@@ -8599,7 +8599,6 @@ static void ggml_compute_forward_timestep_embedding_f32(
         }
         if (dim % 2 != 0 && ith == 0) {
             embed_data[2 * half] = 0.f;
-            embed_data[dim] = 0.f;
         }
     }
 }

ggml/src/ggml-cuda/common.cuh

@@ -75,6 +75,8 @@
 #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
@@ -325,6 +327,20 @@ 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) {
@@ -555,7 +571,7 @@ static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const float2 v
 }
 
 static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const half2 v, const half2 u) {
-#if defined(GGML_USE_HIP) && defined(GCN)
+#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
@@ -567,7 +583,21 @@ static __device__ __forceinline__ void ggml_cuda_mad(float & acc, const half2 v,
     acc += tmpv.x * tmpu.x;
     acc += tmpv.y * tmpu.y;
 #endif // FAST_FP16_AVAILABLE
-#endif // defined(GGML_USE_HIP) && defined(GCN)
+#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) {
@@ -622,6 +652,14 @@ static __device__ __forceinline__ uint32_t fastmodulo(uint32_t n, const uint3 fa
     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/cpy.cu

@@ -441,6 +441,10 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
         return (void*) cpy_flt<cpy_1_flt<nv_bfloat16, nv_bfloat16>>;
     } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32) {
         return (void*) cpy_flt<cpy_1_flt<nv_bfloat16, float>>;
+    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_I32) {
+        return (void*) cpy_flt<cpy_1_flt<float, int32_t>>;
+    } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_F32) {
+        return (void*) cpy_flt<cpy_1_flt<int32_t, float>>;
     } else {
         GGML_ABORT("%s: unsupported type combination (%s to %s)\n", __func__,
                 ggml_type_name(src0->type), ggml_type_name(src1->type));

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

@@ -647,9 +647,7 @@ static __global__ void flash_attn_stream_k_fixup(
 }
 
 template<int D> // D == head size
-#if !defined(GGML_USE_HIP)
 __launch_bounds__(D, 1)
-#endif // !(defined(GGML_USE_HIP)
 static __global__ void flash_attn_combine_results(
         const float  * __restrict__ VKQ_parts,
         const float2 * __restrict__ VKQ_meta,
@@ -692,10 +690,7 @@ static __global__ void flash_attn_combine_results(
     float VKQ_numerator   = 0.0f;
     float VKQ_denominator = 0.0f;
     for (int l = 0; l < parallel_blocks; ++l) {
-        const float diff = meta[l].x - kqmax;
-        float KQ_max_scale = expf(diff);
-        const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD);
-        *((uint32_t *) &KQ_max_scale) &= ftz_mask;
+        const float KQ_max_scale = expf(meta[l].x - kqmax);
 
         VKQ_numerator   += KQ_max_scale * VKQ_parts[l*D + tid];
         VKQ_denominator += KQ_max_scale * meta[l].y;
@@ -836,11 +831,10 @@ void launch_fattn(
         CUDA_CHECK(cudaGetLastError());
     }
 
-    int parallel_blocks = 1;
-
     const dim3 block_dim(warp_size, nwarps, 1);
     int max_blocks_per_sm = 1; // Max. number of active blocks limited by occupancy.
     CUDA_CHECK(cudaOccupancyMaxActiveBlocksPerMultiprocessor(&max_blocks_per_sm, fattn_kernel, block_dim.x * block_dim.y * block_dim.z, nbytes_shared));
+    int parallel_blocks = max_blocks_per_sm;
 
     dim3 blocks_num;
     if (stream_k) {
@@ -862,9 +856,6 @@ void launch_fattn(
         GGML_ASSERT(K->ne[1] % KQ_row_granularity == 0);
         const int ntiles_KQ = K->ne[1] / KQ_row_granularity; // Max. number of parallel blocks limited by tensor size.
 
-        // parallel_blocks should be at least large enough to achieve max. occupancy for a single wave:
-        parallel_blocks = std::max((nsm * max_blocks_per_sm) / ntiles_total, 1);
-
         // parallel_blocks must not be larger than what the tensor size allows:
         parallel_blocks = std::min(parallel_blocks, ntiles_KQ);
 

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

@@ -2,17 +2,30 @@
 #include "fattn-common.cuh"
 #include "fattn-tile.cuh"
 
-#define FATTN_TILE_NTHREADS 256
+// kq_stride == number of KQ rows to process per iteration
+// kq_nbatch == number of K columns to load in parallel for KQ calculation
 
 static int fattn_tile_get_kq_stride_host(const int D, const int ncols, const int cc, const int warp_size) {
     if (GGML_CUDA_CC_IS_AMD(cc)) {
+        if (GGML_CUDA_CC_IS_RDNA(cc)) {
+            switch (D) {
+                case 64:
+                    return 128;
+                case 128:
+                case 256:
+                    return ncols <= 16 ? 128 : 64;
+                default:
+                    GGML_ABORT("fatal error");
+                    return -1;
+            }
+        }
         switch (D) {
             case 64:
-                return ncols <= 16 ? 32 : 64;
+                return ncols == 32 ? 128 : 64;
             case 128:
-                return ncols <= 16 ? 64 : warp_size;
+                return ncols == 32 ? 64 : 32;
             case 256:
-                return 64;
+                return 32;
             default:
                 GGML_ABORT("fatal error");
                 return -1;
@@ -22,7 +35,6 @@ static int fattn_tile_get_kq_stride_host(const int D, const int ncols, const int
         switch (D) {
             case 64:
             case 128:
-                return 128;
             case 256:
                 return ncols <= 16 ? 128 : 64;
             default:
@@ -41,26 +53,38 @@ static int fattn_tile_get_kq_stride_host(const int D, const int ncols, const int
             GGML_ABORT("fatal error");
             return -1;
     }
+    GGML_UNUSED(warp_size);
 }
 
 static constexpr __device__ int fattn_tile_get_kq_stride_device(int D, int ncols, int warp_size) {
 #ifdef GGML_USE_HIP
+#ifdef RDNA
     switch (D) {
         case 64:
-            return ncols <= 16 ? 32 : 64;
+            return 128;
         case 128:
-            return ncols <= 16 ? 64 : warp_size;
         case 256:
-            return 64;
+            return ncols <= 16 ? 128 : 64;
         default:
             return -1;
     }
+#else
+    switch (D) {
+        case 64:
+            return ncols == 32 ? 128 : 64;
+        case 128:
+            return ncols == 32 ? 64 : 32;
+        case 256:
+            return 32;
+        default:
+            return -1;
+    }
+#endif // RDNA
 #else
 #ifdef FAST_FP16_AVAILABLE
     switch (D) {
         case 64:
         case 128:
-            return 128;
         case 256:
             return ncols <= 16 ? 128 : 64;
         default:
@@ -88,9 +112,8 @@ static constexpr __device__ int fattn_tile_get_kq_nbatch_device(int D, int ncols
         case 64:
             return 64;
         case 128:
-            return ncols <= 16 ? 2*warp_size : 128;
         case 256:
-            return ncols <= 16 ? 128 : 2*warp_size;
+            return 128;
         default:
             return -1;
     }
@@ -100,9 +123,8 @@ static constexpr __device__ int fattn_tile_get_kq_nbatch_device(int D, int ncols
         case 64:
             return 64;
         case 128:
-            return ncols <= 16 ? 128 : 64;
         case 256:
-            return ncols <= 16 ? 64 : 128;
+            return 128;
         default:
             return -1;
     }
@@ -122,12 +144,27 @@ static constexpr __device__ int fattn_tile_get_kq_nbatch_device(int D, int ncols
     GGML_UNUSED_VARS(ncols, warp_size);
 }
 
-template<int D, int ncols, bool use_logit_softcap> // D == head size
-#ifdef GGML_USE_HIP
-__launch_bounds__(FATTN_TILE_NTHREADS, 1)
+static int fattn_tile_get_nthreads_host(const int cc, const int ncols) {
+    return 256;
+    GGML_UNUSED_VARS(cc, ncols);
+}
+
+static constexpr __device__ int fattn_tile_get_nthreads_device(int ncols) {
+    return 256;
+    GGML_UNUSED(ncols);
+}
+
+static constexpr __device__ int fattn_tile_get_occupancy_device(int ncols) {
+#ifdef RDNA
+    return 3;
 #else
-__launch_bounds__(FATTN_TILE_NTHREADS, 2)
-#endif // GGML_USE_HIP
+    return ncols <= 16 ? 3 : 2;
+#endif // RDNA
+    GGML_UNUSED(ncols);
+}
+
+template<int D, int ncols, bool use_logit_softcap> // D == head size
+__launch_bounds__(fattn_tile_get_nthreads_device(ncols), fattn_tile_get_occupancy_device(ncols))
 static __global__ void flash_attn_tile(
         const char * __restrict__ Q,
         const char * __restrict__ K,
@@ -173,7 +210,7 @@ static __global__ void flash_attn_tile(
     }
 
     constexpr int warp_size = 32;
-    constexpr int nwarps    = FATTN_TILE_NTHREADS / warp_size;
+    constexpr int nwarps    = fattn_tile_get_nthreads_device(ncols) / warp_size;
     constexpr int kq_stride = fattn_tile_get_kq_stride_device(D, ncols, warp_size);
     static_assert(kq_stride % warp_size == 0, "kq_stride not divisable by warp_size.");
     constexpr int kq_nbatch = fattn_tile_get_kq_nbatch_device(D, ncols, warp_size);
@@ -186,97 +223,140 @@ static __global__ void flash_attn_tile(
     const int sequence = blockIdx.z / ne02;
     const int head = blockIdx.z - sequence*ne02;
     const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix.
-    const float2 * Q_f2   = (const float2 *) (Q    + nb03* sequence         + nb02* head              + nb01*ic0);
-    const half2  * K_h2   = (const half2  *) (K    + nb13* sequence         + nb12*(head / gqa_ratio));
-    const half2  * V_h2   = (const half2  *) (V    + nb13* sequence         + nb12*(head / gqa_ratio)); // K and V have same shape
-    const half   * maskh  = (const half   *) (mask  + nb33*(sequence % ne33)                          + nb31*ic0);
-    const float  * sinksf = (const float  *) (sinks);
+    const float * Q_f    = (const float *) (Q    + nb03* sequence         + nb02* head              + nb01*ic0);
+    const half2 * K_h2   = (const half2 *) (K    + nb13* sequence         + nb12*(head / gqa_ratio));
+    const half2 * V_h2   = (const half2 *) (V    + nb13* sequence         + nb12*(head / gqa_ratio)); // K and V have same shape
+    const half  * maskh  = (const half  *) (mask + nb33*(sequence % ne33)                           + nb31*ic0);
+    const float * sinksf = (const float *) (sinks);
 
     const int stride_KV2 = nb11 / sizeof(half2);
 
     const float slope = get_alibi_slope(max_bias, head, n_head_log2, m0, m1);
 
-    __shared__ float KQ[ncols][kq_stride];
+    constexpr int cpy_nb = ggml_cuda_get_max_cpy_bytes();
+    constexpr int cpy_ne = cpy_nb / 4;
+
+    constexpr int cpw = ncols/nwarps; // cols per warp
+
+    // softmax_iter_j == number of KQ columns for which to calculate softmax in parallel.
+    // KQ is originall 2D but uses a Z-shaped memory pattern for larger reads/writes.
 #ifdef FAST_FP16_AVAILABLE
+    constexpr int softmax_iter_j = cpw < 2*cpy_ne ? cpw : 2*cpy_ne;
+
+    __shared__ half  KQ[ncols/softmax_iter_j][kq_stride][softmax_iter_j];
     __shared__ half2 Q_tmp[ncols][D/2];
-    __shared__ half2 KV_tmp_h2[kq_stride * (kq_nbatch/2 + 1)]; // Padded to avoid memory bank conflicts.
-    half2 VKQ[ncols/nwarps][D/(2*warp_size)] = {{{0.0f, 0.0f}}};
+    __shared__ half2 KV_tmp[kq_stride * (kq_nbatch/2 + cpy_ne)]; // Padded to avoid memory bank conflicts.
+    half2 VKQ[cpw][D/(2*warp_size)] = {{{0.0f, 0.0f}}};
 #else
+    constexpr int softmax_iter_j = cpw < 1*cpy_ne ? cpw : 1*cpy_ne;
+
+    __shared__ float KQ[ncols/softmax_iter_j][kq_stride][softmax_iter_j];
     __shared__ float Q_tmp[ncols][D];
-    __shared__ float KV_tmp_f[kq_stride * (kq_nbatch + 1)]; // Padded to avoid memory bank conflicts.
-    float2 * KV_tmp_f2 = (float2 *) KV_tmp_f;
-    float2 VKQ[ncols/nwarps][D/(2*warp_size)] = {{{0.0f, 0.0f}}};
+    __shared__ float KV_tmp[kq_stride * (kq_nbatch + cpy_ne)]; // Padded to avoid memory bank conflicts.
+    float2 VKQ[cpw][D/(2*warp_size)] = {{{0.0f, 0.0f}}};
 #endif // FAST_FP16_AVAILABLE
+    static_assert(cpw % softmax_iter_j == 0, "bad softmax_iter_j");
 
-
-    float kqmax[ncols/nwarps];
+    float KQ_max[cpw];
 #pragma unroll
     for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-        kqmax[j0/nwarps] = -FLT_MAX/2.0f;
+        KQ_max[j0/nwarps] = -FLT_MAX/2.0f;
     }
-    float kqsum[ncols/nwarps] = {0.0f};
+    float KQ_sum[cpw] = {0.0f};
+
+    // Load Q data, convert to FP16 if fast.
+#pragma unroll
+    for (int j0 = 0; j0 < cpw; ++j0) {
+        const int j = j0 + threadIdx.y*cpw;
+
+        constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size;
 
 #pragma unroll
-    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-        const int j = j0 + threadIdx.y;
+        for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) {
+            float tmp_f[cpy_ne_D] = {0.0f};
+            if (ic0 + j < ne01) {
+                ggml_cuda_memcpy_1<sizeof(tmp_f)>(tmp_f, &Q_f[j*(nb01/sizeof(float)) + i0 + threadIdx.x*cpy_ne_D]);
+            }
 
 #pragma unroll
-        for (int i0 = 0; i0 < D/2; i0 += warp_size) {
-            const float2 tmp = ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i0 + threadIdx.x] : make_float2(0.0f, 0.0f);
+            for (int i1 = 0; i1 < cpy_ne_D; ++i1) {
+                tmp_f[i1] *= scale;
+            }
+
 #ifdef FAST_FP16_AVAILABLE
-            Q_tmp[j][i0 + threadIdx.x] = make_half2(tmp.x * scale, tmp.y * scale);
+            half2 tmp_h2[cpy_ne_D/2];
+#pragma unroll
+            for (int i1 = 0; i1 < cpy_ne_D; i1 += 2) {
+                tmp_h2[i1/2] = make_half2(tmp_f[i1 + 0], tmp_f[i1 + 1]);
+            }
+            ggml_cuda_memcpy_1<sizeof(tmp_h2)>(&Q_tmp[j][i0/2 + threadIdx.x*(cpy_ne_D/2)], tmp_h2);
 #else
-            Q_tmp[j][2*i0             + threadIdx.x] = tmp.x * scale;
-            Q_tmp[j][2*i0 + warp_size + threadIdx.x] = tmp.y * scale;
+            ggml_cuda_memcpy_1<sizeof(tmp_f)> (&Q_tmp[j][i0   + threadIdx.x* cpy_ne_D],    tmp_f);
 #endif // FAST_FP16_AVAILABLE
         }
     }
 
     __syncthreads();
 
+    // Main loop over KV cache:
     const int k_VKQ_max = KV_max ? KV_max[sequence*gridDim.x + blockIdx.x] : ne11;
     for (int k_VKQ_0 = blockIdx.y*kq_stride; k_VKQ_0 < k_VKQ_max; k_VKQ_0 += gridDim.y*kq_stride) {
         // Calculate KQ tile and keep track of new maximum KQ values:
 
-        float kqmax_new[ncols/nwarps];
+        float KQ_max_new[cpw];
 #pragma unroll
-        for (int j = 0; j < ncols/nwarps; ++j) {
-            kqmax_new[j] = kqmax[j];
+        for (int j = 0; j < cpw; ++j) {
+            KQ_max_new[j] = KQ_max[j];
         }
 
-        float sum[kq_stride/warp_size][ncols/nwarps] = {{0.0f}};
+        float KQ_acc[kq_stride/warp_size][cpw] = {{0.0f}}; // Accumulators for KQ matrix multiplication.
 
+        // KQ = K @ Q matrix multiplication:
 #pragma unroll
         for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += kq_nbatch) {
 #pragma unroll
             for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += nwarps) {
                 const int i_KQ = i_KQ_0 + threadIdx.y;
 
-#pragma unroll
-                for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; k_KQ_1 += warp_size) {
-                    const half2 tmp_h2 = K_h2[int64_t(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + k_KQ_1 + threadIdx.x];
 #ifdef FAST_FP16_AVAILABLE
-                    KV_tmp_h2[i_KQ*(kq_nbatch/2 + 1) + k_KQ_1 + threadIdx.x] = tmp_h2;
-#else
-                    const float2 tmp_f2 = __half22float2(tmp_h2);
-                    KV_tmp_f[i_KQ*(kq_nbatch + 1) + 2*k_KQ_1             + threadIdx.x] = tmp_f2.x;
-                    KV_tmp_f[i_KQ*(kq_nbatch + 1) + 2*k_KQ_1 + warp_size + threadIdx.x] = tmp_f2.y;
-#endif // FAST_FP16_AVAILABLE
+                constexpr int cpy_ne_kqnb = cpy_ne < kq_nbatch/(2*warp_size) ? cpy_ne : kq_nbatch/(2*warp_size);
+#pragma unroll
+                for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; k_KQ_1 += warp_size*cpy_ne_kqnb) {
+                    ggml_cuda_memcpy_1<cpy_ne_kqnb*4>(
+                        &KV_tmp[i_KQ*(kq_nbatch/2 + cpy_ne) + k_KQ_1 + threadIdx.x*cpy_ne_kqnb],
+                        &K_h2[int64_t(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + k_KQ_1 + threadIdx.x*cpy_ne_kqnb]);
                 }
+#else
+                constexpr int cpy_ne_kqnb = cpy_ne < kq_nbatch/warp_size ? cpy_ne : kq_nbatch/warp_size;
+#pragma unroll
+                for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch; k_KQ_1 += warp_size*cpy_ne_kqnb) {
+                    half2 tmp_h2[cpy_ne_kqnb/2];
+                    ggml_cuda_memcpy_1<sizeof(tmp_h2)>(
+                        tmp_h2, &K_h2[int64_t(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + k_KQ_1/2 + threadIdx.x*(cpy_ne_kqnb/2)]);
+
+                    float2 tmp_f2[cpy_ne_kqnb/2];
+#pragma unroll
+                    for (int k_KQ_2 = 0; k_KQ_2 < cpy_ne_kqnb/2; ++k_KQ_2) {
+                        tmp_f2[k_KQ_2] = __half22float2(tmp_h2[k_KQ_2]);
+                    }
+                    ggml_cuda_memcpy_1<sizeof(tmp_f2)>(
+                        &KV_tmp[i_KQ*(kq_nbatch + cpy_ne) + k_KQ_1 + threadIdx.x*cpy_ne_kqnb], tmp_f2);
+                }
+#endif // FAST_FP16_AVAILABLE
             }
 
             __syncthreads();
 
 #ifdef FAST_FP16_AVAILABLE
 #pragma unroll
-            for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; ++k_KQ_1) {
-                half2 K_k[kq_stride/warp_size];
-                half2 Q_k[ncols/nwarps];
+            for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch/2; k_KQ_1 += cpy_ne) {
+                half2 K_k[kq_stride/warp_size][cpy_ne];
+                half2 Q_k[cpw][cpy_ne];
 #else
 #pragma unroll
-            for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch; ++k_KQ_1) {
-                float K_k[kq_stride/warp_size];
-                float Q_k[ncols/nwarps];
+            for (int k_KQ_1 = 0; k_KQ_1 < kq_nbatch; k_KQ_1 += cpy_ne) {
+                float K_k[kq_stride/warp_size][cpy_ne];
+                float Q_k[cpw][cpy_ne];
 #endif // FAST_FP16_AVAILABLE
 
 #pragma unroll
@@ -284,27 +364,30 @@ static __global__ void flash_attn_tile(
                     const int i_KQ = i_KQ_0 + threadIdx.x;
 
 #ifdef FAST_FP16_AVAILABLE
-                    K_k[i_KQ_0/warp_size] = KV_tmp_h2[i_KQ*(kq_nbatch/2 + 1) + k_KQ_1];
+                    ggml_cuda_memcpy_1<cpy_nb>(&K_k[i_KQ_0/warp_size], &KV_tmp[i_KQ*(kq_nbatch/2 + cpy_ne) + k_KQ_1]);
 #else
-                    K_k[i_KQ_0/warp_size] = KV_tmp_f [i_KQ*(kq_nbatch   + 1) + k_KQ_1];
+                    ggml_cuda_memcpy_1<cpy_nb>(&K_k[i_KQ_0/warp_size], &KV_tmp[i_KQ*(kq_nbatch   + cpy_ne) + k_KQ_1]);
 #endif // FAST_FP16_AVAILABLE
                 }
 #pragma unroll
-                for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) {
-                    const int j_KQ = j_KQ_0 + threadIdx.y;
+                for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) {
+                    const int j_KQ = j_KQ_0 + threadIdx.y*cpw;
 
 #ifdef FAST_FP16_AVAILABLE
-                    Q_k[j_KQ_0/nwarps] = Q_tmp[j_KQ][k_KQ_0/2 + k_KQ_1];
+                    ggml_cuda_memcpy_1<cpy_nb>(&Q_k[j_KQ_0], &Q_tmp[j_KQ][k_KQ_0/2 + k_KQ_1]);
 #else
-                    Q_k[j_KQ_0/nwarps] = Q_tmp[j_KQ][k_KQ_0   + k_KQ_1];
+                    ggml_cuda_memcpy_1<cpy_nb>(&Q_k[j_KQ_0], &Q_tmp[j_KQ][k_KQ_0   + k_KQ_1]);
 #endif // FAST_FP16_AVAILABLE
                 }
 
 #pragma unroll
                 for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) {
 #pragma unroll
-                    for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) {
-                        ggml_cuda_mad(sum[i_KQ_0/warp_size][j_KQ_0/nwarps], K_k[i_KQ_0/warp_size], Q_k[j_KQ_0/nwarps]);
+                    for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) {
+#pragma unroll
+                        for (int k = 0; k < cpy_ne; ++k) {
+                            ggml_cuda_mad(KQ_acc[i_KQ_0/warp_size][j_KQ_0], K_k[i_KQ_0/warp_size][k], Q_k[j_KQ_0][k]);
+                        }
                     }
                 }
             }
@@ -314,64 +397,77 @@ static __global__ void flash_attn_tile(
             }
         }
 
+        // Apply logit softcap, mask, update KQ_max:
 #pragma unroll
         for (int i_KQ_0 = 0; i_KQ_0 < kq_stride; i_KQ_0 += warp_size) {
             const int i_KQ = i_KQ_0 + threadIdx.x;
 
 #pragma unroll
-            for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) {
-                const int j_KQ = j_KQ_0 + threadIdx.y;
+            for (int j_KQ_0 = 0; j_KQ_0 < cpw; ++j_KQ_0) {
+                const int j_KQ = j_KQ_0 + threadIdx.y*cpw;
 
                 if (use_logit_softcap) {
-                    sum[i_KQ_0/warp_size][j_KQ_0/nwarps] = logit_softcap * tanhf(sum[i_KQ_0/warp_size][j_KQ_0/nwarps]);
+                    KQ_acc[i_KQ_0/warp_size][j_KQ_0] = logit_softcap * tanhf(KQ_acc[i_KQ_0/warp_size][j_KQ_0]);
                 }
 
-                sum[i_KQ_0/warp_size][j_KQ_0/nwarps] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
+                KQ_acc[i_KQ_0/warp_size][j_KQ_0] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f;
 
-                kqmax_new[j_KQ_0/nwarps] = fmaxf(kqmax_new[j_KQ_0/nwarps], sum[i_KQ_0/warp_size][j_KQ_0/nwarps]);
-
-                KQ[j_KQ][i_KQ] = sum[i_KQ_0/warp_size][j_KQ_0/nwarps];
+                KQ_max_new[j_KQ_0] = fmaxf(KQ_max_new[j_KQ_0], KQ_acc[i_KQ_0/warp_size][j_KQ_0]);
             }
         }
 
         __syncthreads();
 
+        // Calculate KQ softmax, write to shared KQ buffer, re-scale VKQ accumulators:
 #pragma unroll
-        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-            const int j = j0 + threadIdx.y;
+        for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) {
+#ifdef FAST_FP16_AVAILABLE
+            half  tmp[kq_stride/warp_size][softmax_iter_j];
+#else
+            float tmp[kq_stride/warp_size][softmax_iter_j];
+#endif // FAST_FP16_AVAILABLE
 
-            kqmax_new[j0/nwarps] = warp_reduce_max<warp_size>(kqmax_new[j0/nwarps]);
-            const float KQ_max_scale = expf(kqmax[j0/nwarps] - kqmax_new[j0/nwarps]);
-            kqmax[j0/nwarps] = kqmax_new[j0/nwarps];
+#pragma unroll
+            for (int j1 = 0; j1 < softmax_iter_j; ++j1) {
+                KQ_max_new[j0+j1] = warp_reduce_max<warp_size>(KQ_max_new[j0+j1]);
+                const float KQ_max_scale = expf(KQ_max[j0+j1] - KQ_max_new[j0+j1]);
+                KQ_max[j0+j1] = KQ_max_new[j0+j1];
+
+                float KQ_sum_add = 0.0f;
+#pragma unroll
+                for (int i0 = 0; i0 < kq_stride; i0 += warp_size) {
+                    const float val = expf(KQ_acc[i0/warp_size][j0+j1] - KQ_max[j0+j1]);
+                    KQ_sum_add += val;
+                    tmp[i0/warp_size][j1] = val;
+                }
+                KQ_sum[j0+j1] = KQ_sum[j0+j1]*KQ_max_scale + KQ_sum_add;
+
+#ifdef FAST_FP16_AVAILABLE
+                const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale);
+#pragma unroll
+                for (int i0 = 0; i0 < D/2; i0 += warp_size) {
+                    VKQ[j0+j1][i0/warp_size] *= KQ_max_scale_h2;
+                }
+#else
+#pragma unroll
+                for (int i0 = 0; i0 < D/2; i0 += warp_size) {
+                    VKQ[j0+j1][i0/warp_size].x *= KQ_max_scale;
+                    VKQ[j0+j1][i0/warp_size].y *= KQ_max_scale;
+                }
+#endif // FAST_FP16_AVAILABLE
+            }
 
-            float kqsum_add = 0.0f;
 #pragma unroll
             for (int i0 = 0; i0 < kq_stride; i0 += warp_size) {
                 const int i = i0 + threadIdx.x;
 
-                const float diff = KQ[j][i] - kqmax[j0/nwarps];
-                const float val = expf(diff);
-                kqsum_add += val;
-                KQ[j][i] = val;
+                ggml_cuda_memcpy_1<sizeof(tmp[0])>(
+                    KQ[j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j)][i], tmp[i0/warp_size]);
             }
-            kqsum[j0/nwarps] = kqsum[j0/nwarps]*KQ_max_scale + kqsum_add;
-
-#ifdef FAST_FP16_AVAILABLE
-            const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale);
-#pragma unroll
-            for (int i0 = 0; i0 < D/2; i0 += warp_size) {
-                VKQ[j0/nwarps][i0/warp_size] *= KQ_max_scale_h2;
-            }
-#else
-#pragma unroll
-            for (int i0 = 0; i0 < D/2; i0 += warp_size) {
-                VKQ[j0/nwarps][i0/warp_size].x *= KQ_max_scale;
-                VKQ[j0/nwarps][i0/warp_size].y *= KQ_max_scale;
-            }
-#endif // FAST_FP16_AVAILABLE
         }
 
-        constexpr int V_cols_per_iter = kq_stride*kq_nbatch / D;
+        // VKQ = V @ KQ matrix multiplication:
+        constexpr int V_cols_per_iter = kq_stride*kq_nbatch / D; // Number of V columns that fit in SRAM for K.
         static_assert(kq_stride % V_cols_per_iter == 0, "bad V_cols_per_iter");
 #pragma unroll
         for (int k0 = 0; k0 < kq_stride; k0 += V_cols_per_iter) {
@@ -379,66 +475,96 @@ static __global__ void flash_attn_tile(
             for (int k1 = 0; k1 < V_cols_per_iter; k1 += nwarps) {
                 const int k_tile = k1 + threadIdx.y;
 
-#pragma unroll
-                for (int i0 = 0; i0 < D/2; i0 += warp_size) {
-                    const int i = i0 + threadIdx.x;
-
-                    const half2 tmp = V_h2[int64_t(k_VKQ_0 + k0 + k_tile)*stride_KV2 + i];
 #ifdef FAST_FP16_AVAILABLE
-                    KV_tmp_h2[k_tile*(D/2) + i] = tmp;
-#else
-                    KV_tmp_f2[k_tile*(D/2) + i] = __half22float2(tmp);
-#endif // FAST_FP16_AVAILABLE
+                constexpr int cpy_ne_D = cpy_ne < D/(2*warp_size) ? cpy_ne : D/(2*warp_size);
+#pragma unroll
+                for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) {
+                    ggml_cuda_memcpy_1<cpy_ne_D*4>(
+                        &KV_tmp[k_tile*(D/2) + i0 + threadIdx.x*cpy_ne_D],
+                        &V_h2[int64_t(k_VKQ_0 + k0 + k_tile)*stride_KV2 + i0 + threadIdx.x*cpy_ne_D]);
                 }
+#else
+                constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size;
+#pragma unroll
+                for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) {
+                    half2 tmp_h2[cpy_ne_D/2];
+                    ggml_cuda_memcpy_1<sizeof(tmp_h2)>(
+                        tmp_h2, &V_h2[int64_t(k_VKQ_0 + k0 + k_tile)*stride_KV2 + i0/2 + threadIdx.x*(cpy_ne_D/2)]);
+
+                    float2 tmp_f2[cpy_ne_D/2];
+#pragma unroll
+                    for (int i1 = 0; i1 < cpy_ne_D/2; ++i1) {
+                        tmp_f2[i1] = __half22float2(tmp_h2[i1]);
+                    }
+                    ggml_cuda_memcpy_1<sizeof(tmp_f2)>(
+                        &KV_tmp[k_tile*D + i0 + threadIdx.x*cpy_ne_D], tmp_f2);
+                }
+#endif // FAST_FP16_AVAILABLE
             }
 
             __syncthreads();
 
+#ifdef FAST_FP16_AVAILABLE
 #pragma unroll
             for (int k1 = 0; k1 < V_cols_per_iter; ++k1) {
-#ifdef FAST_FP16_AVAILABLE
                 half2 V_k[(D/2)/warp_size];
-                half2 KQ_k[ncols/nwarps];
-#else
-                float2 V_k[(D/2)/warp_size];
-                float  KQ_k[ncols/nwarps];
-#endif // FAST_FP16_AVAILABLE
+                half2 KQ_k[cpw];
 
+                constexpr int cpy_ne_D = cpy_ne/2 < (D/2)/warp_size ? cpy_ne/2 : (D/2)/warp_size;
 #pragma unroll
-                for (int i0 = 0; i0 < D/2; i0 += warp_size) {
-                    const int i = i0 + threadIdx.x;
-
-#ifdef FAST_FP16_AVAILABLE
-                    V_k[i0/warp_size] = KV_tmp_h2[k1*(D/2) + i];
-#else
-                    V_k[i0/warp_size] = KV_tmp_f2[k1*(D/2) + i];
-#endif // FAST_FP16_AVAILABLE
+                for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) {
+                    ggml_cuda_memcpy_1<cpy_ne_D*4>(&V_k[i0/warp_size], &KV_tmp[k1*(D/2) + i0 + threadIdx.x*cpy_ne_D]);
                 }
 #pragma unroll
-                for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-                    const int j = j0 + threadIdx.y;
+                for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) {
+                    const int j = j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j);
 
-#ifdef FAST_FP16_AVAILABLE
-                    const float tmp = KQ[j][k0 + k1];
-                    KQ_k[j0/nwarps] = make_half2(tmp, tmp);
-#else
-                    KQ_k[j0/nwarps] = KQ[j][k0 + k1];
-#endif // FAST_FP16_AVAILABLE
+                    half tmp[softmax_iter_j];
+                    ggml_cuda_memcpy_1<softmax_iter_j*sizeof(half)>(
+                        &tmp, KQ[j][k0 + k1]);
+#pragma unroll
+                    for (int j1 = 0; j1 < softmax_iter_j; ++j1) {
+                        KQ_k[j0+j1] = __half2half2(tmp[j1]);
+                    }
                 }
 
 #pragma unroll
                 for (int i0 = 0; i0 < D/2; i0 += warp_size) {
 #pragma unroll
-                    for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-#ifdef FAST_FP16_AVAILABLE
-                        VKQ[j0/nwarps][i0/warp_size]   += V_k[i0/warp_size]  *KQ_k[j0/nwarps];
-#else
-                        VKQ[j0/nwarps][i0/warp_size].x += V_k[i0/warp_size].x*KQ_k[j0/nwarps];
-                        VKQ[j0/nwarps][i0/warp_size].y += V_k[i0/warp_size].y*KQ_k[j0/nwarps];
-#endif // FAST_FP16_AVAILABLE
+                    for (int j0 = 0; j0 < cpw; ++j0) {
+                        VKQ[j0][i0/warp_size] += V_k[i0/warp_size]*KQ_k[j0];
                     }
                 }
             }
+#else
+#pragma unroll
+            for (int k1 = 0; k1 < V_cols_per_iter; ++k1) {
+                float2 V_k[(D/2)/warp_size];
+                float  KQ_k[cpw];
+
+                constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size;
+#pragma unroll
+                for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) {
+                    ggml_cuda_memcpy_1<cpy_ne_D*4>(&V_k[i0/(2*warp_size)], &KV_tmp[k1*D + i0 + threadIdx.x*cpy_ne_D]);
+                }
+#pragma unroll
+                for (int j0 = 0; j0 < cpw; j0 += softmax_iter_j) {
+                    const int j = j0/softmax_iter_j + threadIdx.y*(cpw/softmax_iter_j);
+
+                    ggml_cuda_memcpy_1<softmax_iter_j*sizeof(float)>(
+                        &KQ_k[j0], KQ[j][k0 + k1]);
+                }
+
+#pragma unroll
+                for (int i0 = 0; i0 < D/2; i0 += warp_size) {
+#pragma unroll
+                    for (int j0 = 0; j0 < cpw; ++j0) {
+                        VKQ[j0][i0/warp_size].x += V_k[i0/warp_size].x*KQ_k[j0];
+                        VKQ[j0][i0/warp_size].y += V_k[i0/warp_size].y*KQ_k[j0];
+                    }
+                }
+            }
+#endif // FAST_FP16_AVAILABLE
 
             __syncthreads();
         }
@@ -450,69 +576,92 @@ static __global__ void flash_attn_tile(
         const float sink = sinksf[head];
 
 #pragma unroll
-        for (int j0 = 0; j0 < ncols; j0 += nwarps) {
-            float kqmax_new_j = fmaxf(kqmax[j0/nwarps], sink);
-            kqmax_new_j = warp_reduce_max<warp_size>(kqmax_new_j);
+        for (int j0 = 0; j0 < cpw; ++j0) {
+            float KQ_max_new_j = fmaxf(KQ_max[j0], sink);
+            KQ_max_new_j = warp_reduce_max<warp_size>(KQ_max_new_j);
 
-            const float KQ_max_scale = expf(kqmax[j0/nwarps] - kqmax_new_j);
-            kqmax[j0/nwarps] = kqmax_new_j;
+            const float KQ_max_scale = expf(KQ_max[j0] - KQ_max_new_j);
+            KQ_max[j0] = KQ_max_new_j;
 
-            const float val = expf(sink - kqmax[j0/nwarps]);
-            kqsum[j0/nwarps] = kqsum[j0/nwarps] * KQ_max_scale;
+            const float val = expf(sink - KQ_max[j0]);
+            KQ_sum[j0] = KQ_sum[j0] * KQ_max_scale;
             if (threadIdx.x == 0) {
-                kqsum[j0/nwarps] += val;
+                KQ_sum[j0] += val;
             }
 
 #ifdef FAST_FP16_AVAILABLE
             const half2 KQ_max_scale_h2 = make_half2(KQ_max_scale, KQ_max_scale);
 #pragma unroll
             for (int i0 = 0; i0 < D/2; i0 += warp_size) {
-                VKQ[j0/nwarps][i0/warp_size] *= KQ_max_scale_h2;
+                VKQ[j0][i0/warp_size] *= KQ_max_scale_h2;
             }
 #else
 #pragma unroll
             for (int i0 = 0; i0 < D/2; i0 += warp_size) {
-                VKQ[j0/nwarps][i0/warp_size].x *= KQ_max_scale;
-                VKQ[j0/nwarps][i0/warp_size].y *= KQ_max_scale;
+                VKQ[j0][i0/warp_size].x *= KQ_max_scale;
+                VKQ[j0][i0/warp_size].y *= KQ_max_scale;
             }
 #endif // FAST_FP16_AVAILABLE
         }
     }
 
-    float2 * dst2 = (float2 *) dst;
-
 #pragma unroll
-    for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) {
-        const int j_VKQ = j_VKQ_0 + threadIdx.y;
+    for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) {
+        KQ_sum[j_VKQ_0] = warp_reduce_sum<warp_size>(KQ_sum[j_VKQ_0]);
+    }
+    if (gridDim.y == 1) {
+#pragma unroll
+        for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) {
+#ifdef FAST_FP16_AVAILABLE
+            const half2 KQ_sum_j_inv = make_half2(1.0f/KQ_sum[j_VKQ_0], 1.0f/KQ_sum[j_VKQ_0]);
+#pragma unroll
+            for (int i = 0; i < (D/2)/warp_size; ++i) {
+                VKQ[j_VKQ_0][i] *= KQ_sum_j_inv;
+            }
+#else
+            const float KQ_sum_j_inv = 1.0f/KQ_sum[j_VKQ_0];
+#pragma unroll
+            for (int i = 0; i < (D/2)/warp_size; ++i) {
+                VKQ[j_VKQ_0][i].x *= KQ_sum_j_inv;
+                VKQ[j_VKQ_0][i].y *= KQ_sum_j_inv;
+            }
+#endif // FAST_FP16_AVAILABLE
+        }
+    }
+
+    // Write back results:
+#pragma unroll
+    for (int j_VKQ_0 = 0; j_VKQ_0 < cpw; ++j_VKQ_0) {
+        const int j_VKQ = j_VKQ_0 + threadIdx.y*cpw;
 
         if (ic0 + j_VKQ >= ne01) {
             return;
         }
 
-        float kqsum_j = kqsum[j_VKQ_0/nwarps];
-        kqsum_j = warp_reduce_sum<warp_size>(kqsum_j);
-
         const int j_dst_unrolled = ((sequence*ne01 + ic0 + j_VKQ)*ne02 + head)*gridDim.y + blockIdx.y;
 
-#pragma unroll
-        for (int i00 = 0; i00 < D/2; i00 += warp_size) {
-            const int i0 = i00 + threadIdx.x;
-
 #ifdef FAST_FP16_AVAILABLE
-            float2 dst_val = __half22float2(VKQ[j_VKQ_0/nwarps][i0/warp_size]);
+        constexpr int cpy_ne_D = cpy_ne/2 < (D/2)/warp_size ? cpy_ne/2 : (D/2)/warp_size;
+#pragma unroll
+        for (int i0 = 0; i0 < D/2; i0 += warp_size*cpy_ne_D) {
+            float2 tmp[cpy_ne_D];
+#pragma unroll
+            for (int i1 = 0; i1 < cpy_ne_D; ++i1) {
+                tmp[i1] = __half22float2(VKQ[j_VKQ_0][i0/warp_size + i1]);
+            }
+            ggml_cuda_memcpy_1<sizeof(tmp)>(&dst[j_dst_unrolled*D + 2*i0 + threadIdx.x*(2*cpy_ne_D)], tmp);
+        }
 #else
-            float2 dst_val = VKQ[j_VKQ_0/nwarps][i0/warp_size];
+        constexpr int cpy_ne_D = cpy_ne < D/warp_size ? cpy_ne : D/warp_size;
+#pragma unroll
+        for (int i0 = 0; i0 < D; i0 += warp_size*cpy_ne_D) {
+            ggml_cuda_memcpy_1<cpy_ne_D*4>(
+                &dst[j_dst_unrolled*D + i0 + threadIdx.x*cpy_ne_D], &VKQ[j_VKQ_0][i0/(2*warp_size)]);
+        }
 #endif // FAST_FP16_AVAILABLE
 
-            if (gridDim.y == 1) {
-                dst_val.x /= kqsum_j;
-                dst_val.y /= kqsum_j;
-            }
-            dst2[j_dst_unrolled*(D/2) + i0] = dst_val;
-        }
-
         if (gridDim.y != 1 && threadIdx.x == 0) {
-            dst_meta[j_dst_unrolled] = make_float2(kqmax[j_VKQ_0/nwarps], kqsum_j);
+            dst_meta[j_dst_unrolled] = make_float2(KQ_max[j_VKQ_0], KQ_sum[j_VKQ_0]);
         }
     }
 #else
@@ -533,15 +682,29 @@ template <int D, bool use_logit_softcap>
 static void launch_fattn_tile_switch_ncols(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * Q = dst->src[0];
 
-    const int id                 = ggml_cuda_get_device();
-    const int cc                 = ggml_cuda_info().devices[id].cc;
-    const int warp_size          = 32;
-    const int nwarps             = FATTN_TILE_NTHREADS / warp_size;
+    const int id        = ggml_cuda_get_device();
+    const int cc        = ggml_cuda_info().devices[id].cc;
+    const int warp_size = 32;
 
     constexpr size_t nbytes_shared = 0;
 
+#ifdef GGML_USE_HIP
+    if constexpr (D <= 128) {
+        if (Q->ne[1] > 32) {
+            constexpr int cols_per_block = 64;
+            const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size;
+            fattn_kernel_t fattn_kernel = flash_attn_tile<D, cols_per_block, use_logit_softcap>;
+            const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size);
+            launch_fattn<D, cols_per_block, 1>
+                (ctx, dst, fattn_kernel, nwarps, nbytes_shared, kq_stride, true, true, false, warp_size);
+            return;
+        }
+    }
+#endif // GGML_USE_HIP
+
     if (Q->ne[1] > 16) {
         constexpr int cols_per_block = 32;
+        const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size;
         fattn_kernel_t fattn_kernel = flash_attn_tile<D, cols_per_block, use_logit_softcap>;
         const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size);
         launch_fattn<D, cols_per_block, 1>
@@ -550,6 +713,7 @@ static void launch_fattn_tile_switch_ncols(ggml_backend_cuda_context & ctx, ggml
     }
 
     constexpr int cols_per_block = 16;
+    const int nwarps = fattn_tile_get_nthreads_host(cc, cols_per_block) / warp_size;
     fattn_kernel_t fattn_kernel = flash_attn_tile<D, cols_per_block, use_logit_softcap>;
     const int kq_stride = fattn_tile_get_kq_stride_host(D, cols_per_block, cc, warp_size);
     launch_fattn<D, cols_per_block, 1>

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

@@ -3140,7 +3140,7 @@ static const ggml_backend_i ggml_backend_cuda_interface = {
     /* .graph_compute           = */ ggml_backend_cuda_graph_compute,
     /* .event_record            = */ ggml_backend_cuda_event_record,
     /* .event_wait              = */ ggml_backend_cuda_event_wait,
-    /* .optimize_graph          = */ NULL,
+    /* .graph_optimize          = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_cuda_guid() {
@@ -3210,6 +3210,7 @@ struct ggml_backend_cuda_device_context {
     int device;
     std::string name;
     std::string description;
+    std::string pci_bus_id;
 };
 
 static const char * ggml_backend_cuda_device_get_name(ggml_backend_dev_t dev) {
@@ -3234,9 +3235,12 @@ static enum ggml_backend_dev_type ggml_backend_cuda_device_get_type(ggml_backend
 }
 
 static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
+    ggml_backend_cuda_device_context * ctx = (ggml_backend_cuda_device_context *)dev->context;
+
     props->name        = ggml_backend_cuda_device_get_name(dev);
     props->description = ggml_backend_cuda_device_get_description(dev);
     props->type        = ggml_backend_cuda_device_get_type(dev);
+    props->device_id   = ctx->pci_bus_id.empty() ? nullptr : ctx->pci_bus_id.c_str();
     ggml_backend_cuda_device_get_memory(dev, &props->memory_free, &props->memory_total);
 
     bool host_buffer = getenv("GGML_CUDA_NO_PINNED") == nullptr;
@@ -3804,6 +3808,10 @@ ggml_backend_reg_t ggml_backend_cuda_reg() {
                 CUDA_CHECK(cudaGetDeviceProperties(&prop, i));
                 dev_ctx->description = prop.name;
 
+                char pci_bus_id[16] = {};
+                snprintf(pci_bus_id, sizeof(pci_bus_id), "%04x:%02x:%02x.0", prop.pciDomainID, prop.pciBusID, prop.pciDeviceID);
+                dev_ctx->pci_bus_id = pci_bus_id;
+
                 ggml_backend_dev_t dev = new ggml_backend_device {
                     /* .iface   = */ ggml_backend_cuda_device_interface,
                     /* .reg     = */ &reg,

ggml/src/ggml-cuda/im2col.cu

@@ -122,11 +122,14 @@ static  __global__ void im2col_3d_kernel(
         int64_t OH_OW, int64_t KD_KH_KW, int64_t ID_IH_IW, int64_t KH_KW, int64_t IH_IW, int64_t IC_ID_IH_IW,
         int64_t IC_KD_KH_KW, int64_t OW_KD_KH_KW, int64_t OD_OH_OW_IC_KD_KH_KW, int64_t OH_OW_IC_KD_KH_KW,
         int64_t OW_IC_KD_KH_KW, int64_t N_OD_OH, int64_t OD_OH,
+        int64_t stride_q, int64_t stride_z, int64_t stride_y, int64_t stride_x,
         int s0, int s1, int s2, int p0, int p1, int p2, int d0, int d1, int d2) {
     const int64_t i = threadIdx.x + blockIdx.x * blockDim.x;
     if (i >= IC_KD_KH_KW) {
         return;
     }
+    GGML_UNUSED(N); GGML_UNUSED(OC); GGML_UNUSED(OH_OW); GGML_UNUSED(OD); GGML_UNUSED(OW); GGML_UNUSED(KD); GGML_UNUSED(KH);
+    GGML_UNUSED(ID_IH_IW); GGML_UNUSED(IH_IW); GGML_UNUSED(IC_ID_IH_IW); GGML_UNUSED(OW_KD_KH_KW);
 
     const int64_t iic = i / KD_KH_KW;
     const int64_t ikd = (i - iic * KD_KH_KW) / KH_KW;
@@ -148,7 +151,7 @@ static  __global__ void im2col_3d_kernel(
         if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW || iid < 0 || iid >= ID) {
             dst[offset_dst] = 0.0f;
         } else {
-            const int64_t offset_src = in*IC_ID_IH_IW + iic*ID_IH_IW + iid*IH_IW + iih*IW + iiw;
+            const int64_t offset_src = ((in * IC + iic) * stride_q) + (iid * stride_z) + (iih * stride_y) + (iiw * stride_x);
             dst[offset_dst] = src[offset_src];
         }
     }
@@ -159,6 +162,7 @@ template <typename T>
 static void im2col_3d_cuda(const float * src, T* dst,
     int64_t N, int64_t IC, int64_t ID, int64_t IH, int64_t IW, int64_t OC,
     int64_t KD, int64_t KH, int64_t KW, int64_t OD, int64_t OH, int64_t OW,
+    int64_t stride_q, int64_t stride_z, int64_t stride_y, int64_t stride_x,
     int s0, int s1, int s2, int p0, int p1, int p2, int d0, int d1, int d2, cudaStream_t stream) {
     const int64_t OH_OW = OH*OW;
     const int64_t KD_KH_KW = KD*KH*KW;
@@ -179,23 +183,30 @@ static void im2col_3d_cuda(const float * src, T* dst,
                                                                                            OH_OW, KD_KH_KW, ID_IH_IW, KH_KW, IH_IW, IC_ID_IH_IW,
                                                                                            IC_KD_KH_KW, OW_KD_KH_KW, OD_OH_OW_IC_KD_KH_KW,
                                                                                            OH_OW_IC_KD_KH_KW, OW_IC_KD_KH_KW, N_OD_OH, OD_OH,
+                                                                                           stride_q, stride_z, stride_y, stride_x,
                                                                                            s0, s1, s2, p0, p1, p2, d0, d1, d2);
 }
 
 static void im2col_3d_cuda_f16(const float * src, half * dst,
     int64_t N, int64_t IC, int64_t ID, int64_t IH, int64_t IW, int64_t OC,
     int64_t KD, int64_t KH, int64_t KW, int64_t OD, int64_t OH, int64_t OW,
+    int64_t stride_q, int64_t stride_z, int64_t stride_y, int64_t stride_x,
     int s0, int s1, int s2, int p0, int p1, int p2, int d0, int d1, int d2, cudaStream_t stream) {
 
-    im2col_3d_cuda<half>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+    im2col_3d_cuda<half>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW,
+                         stride_q, stride_z, stride_y, stride_x,
+                         s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
 }
 
 static void im2col_3d_cuda_f32(const float * src, float * dst,
     int64_t N, int64_t IC, int64_t ID, int64_t IH, int64_t IW, int64_t OC,
     int64_t KD, int64_t KH, int64_t KW, int64_t OD, int64_t OH, int64_t OW,
+    int64_t stride_q, int64_t stride_z, int64_t stride_y, int64_t stride_x,
     int s0, int s1, int s2, int p0, int p1, int p2, int d0, int d1, int d2, cudaStream_t stream) {
 
-    im2col_3d_cuda<float>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+    im2col_3d_cuda<float>(src, dst, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW,
+                          stride_q, stride_z, stride_y, stride_x,
+                          s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
 }
 
 void ggml_cuda_op_im2col_3d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
@@ -235,9 +246,19 @@ void ggml_cuda_op_im2col_3d(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     const int64_t OH = ne2;
     const int64_t OW = ne1;
 
+    const size_t  es       = ggml_element_size(src1);
+    const int64_t stride_x = src1->nb[0] / es;
+    const int64_t stride_y = src1->nb[1] / es;
+    const int64_t stride_z = src1->nb[2] / es;
+    const int64_t stride_q = src1->nb[3] / es;
+
     if(dst->type == GGML_TYPE_F16) {
-        im2col_3d_cuda_f16(src1_d, (half *) dst_d, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+        im2col_3d_cuda_f16(src1_d, (half *) dst_d, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW,
+                           stride_q, stride_z, stride_y, stride_x,
+                           s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
     } else {
-        im2col_3d_cuda_f32(src1_d, (float *) dst_d, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW, s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
+        im2col_3d_cuda_f32(src1_d, (float *) dst_d, N, IC, ID, IH, IW, OC, KD, KH, KW, OD, OH, OW,
+                           stride_q, stride_z, stride_y, stride_x,
+                           s0, s1, s2, p0, p1, p2, d0, d1, d2, stream);
     }
 }

ggml/src/ggml-cuda/mmf.cuh

@@ -57,31 +57,33 @@ static __global__ void mul_mat_f(
     T * tile_xy = (T *) compute_base + threadIdx.y*(tile_A::I * tile_k_padded);
 
     if constexpr (has_ids) {
-        __shared__ int has_any;
-        if (threadIdx.y == 0) {
-            int local_has_any = 0;
-            for (int j = threadIdx.x; j < cols_per_block; j += warp_size) {
-                int slot = -1;
-                for (int k = 0; k < nchannels_dst; ++k) {
-                    const int idv = ids[j*stride_row_id + k*stride_col_id];
-                    if (idv == expert_idx) {
-                        slot = k;
-                        break;
-                    }
-                }
-                if (j < cols_per_block) {
-                    local_has_any |= (slot >= 0);
-                    slot_map[j] = slot;
+        int found = 0;
+
+        for (int j0 = 0; j0 < cols_per_block; j0 += nwarps) {
+            const int j = j0 + threadIdx.y;
+            const int32_t * __restrict__ id_row = ids + j*stride_row_id;
+
+            if (threadIdx.x == 0) {
+                slot_map[j] = -1;
+            }
+
+            for (int k = threadIdx.x; k < nchannels_dst; k += warp_size) {
+                int match = id_row[k*stride_col_id] == expert_idx;
+
+                if (match) {
+                    slot_map[j] = k;
+                    found = 1;
+                    break;
                 }
             }
-            has_any = warp_reduce_any(local_has_any);
         }
-        __syncthreads();
-        if (has_any == 0) {
+
+        if (!__syncthreads_or(found)) {
             return;
         }
     }
 
+
     for (int col = threadIdx.y*warp_size + threadIdx.x; col < ncols; col += nwarps*warp_size) {
         tile_A A[ntA][warp_size / tile_A::J];
 #pragma unroll
@@ -106,14 +108,7 @@ static __global__ void mul_mat_f(
                     if constexpr (!has_ids) {
                         tile_xy[j0*tile_k_padded + threadIdx.x] = j < cols_per_block ? y[j*stride_col_y + col] : 0.0f;
                     } else {
-                        float val = 0.0f;
-                        if (j < cols_per_block) {
-                            const int slot = slot_map[j];
-                            if (slot >= 0) {
-                                val = y[slot*stride_channel_y + j*stride_col_y + col];
-                            }
-                        }
-                        tile_xy[j0*tile_k_padded + threadIdx.x] = val;
+                        tile_xy[j0*tile_k_padded + threadIdx.x] = j < cols_per_block ? y[slot_map[j]*stride_channel_y + j*stride_col_y + col] : 0.0f;
                     }
                 }
             } else if constexpr (std::is_same_v<T, half2> || std::is_same_v<T, nv_bfloat162>) {
@@ -125,14 +120,7 @@ static __global__ void mul_mat_f(
                         const float2 tmp = j < cols_per_block ? y2[j*stride_col_y + col] : make_float2(0.0f, 0.0f);
                         tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y};
                     } else {
-                        float2 tmp = make_float2(0.0f, 0.0f);
-                        if (j < cols_per_block) {
-                            const int slot = slot_map[j];
-                            if (slot >= 0) {
-                                const float2 * y2_slot = (const float2 *)(y + slot*stride_channel_y);
-                                tmp = y2_slot[j*stride_col_y + col];
-                            }
-                        }
+                        float2 tmp = j < cols_per_block && slot_map[j] >= 0 ? *(const float2*) &y[slot_map[j]*stride_channel_y + 2*(j*stride_col_y + col)] : make_float2(0.0f, 0.0f);
                         tile_xy[j0*tile_k_padded + threadIdx.x] = {tmp.x, tmp.y};
                     }
                 }
@@ -221,7 +209,7 @@ static inline void mul_mat_f_switch_ids(
         const dim3 & block_nums, const dim3 & block_dims, const int nbytes_shared_total, cudaStream_t stream) {
     if (ids) {
         mul_mat_f<T, MMF_ROWS_PER_BLOCK, cols_per_block, nwarps, true><<<block_nums, block_dims, nbytes_shared_total, stream>>>
-            (x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
+             (x, y, ids, dst, ncols_x, nchannels_dst, stride_row, stride_col_y, stride_col_dst,
              stride_col_id, stride_row_id, channel_ratio, stride_channel_x, stride_channel_y, stride_channel_dst,
              sample_ratio, stride_sample_x, stride_sample_y, stride_sample_dst);
     } else {

ggml/src/ggml-cuda/pad_reflect_1d.cu

@@ -1,82 +1,89 @@
 #include "pad_reflect_1d.cuh"
 
-static __global__ void pad_reflect_1d_kernel_f32(
-    const void * __restrict__ src0,
-    void * __restrict__ dst,
-    const int64_t ne0,
-    const int64_t ne00,
-    const int64_t ne01,
-    const int64_t ne02,
-    const int64_t ne03,
-    const int64_t nb00,
-    const int64_t nb01,
-    const int64_t nb02,
-    const int64_t nb03,
-    const int64_t nb0,
-    const int64_t nb1,
-    const int64_t nb2,
-    const int64_t nb3,
-    const int p0,
-    const int p1) {
-
+static __global__ __launch_bounds__(CUDA_PAD_REFLECT_1D_BLOCK_SIZE, 1) void
+    pad_reflect_1d_kernel_f32(
+        const void * __restrict__ src0,
+        void * __restrict__       dst,
+        const int64_t             ne0,
+        const int64_t             ne00,
+        const uint3               ne01,
+        const int64_t             ne02,
+        const int64_t             ne03,
+        const int64_t             nb00,
+        const int64_t             nb01,
+        const int64_t             nb02,
+        const int64_t             nb03,
+        const int64_t             nb0,
+        const int64_t             nb1,
+        const int64_t             nb2,
+        const int64_t             nb3,
+        const int                 p0,
+        const int                 p1) {
     const int64_t i3 = blockIdx.z;
     const int64_t i2 = blockIdx.y;
-    const int64_t i1 = blockIdx.x;
 
-    if (i1 >= ne01 || i2 >= ne02 || i3 >= ne03) {
+    const uint2   div_mod_packed = fast_div_modulo(blockIdx.x, ne01);
+    const int64_t tile1          = div_mod_packed.y;  // i1
+    const int64_t tile0          = div_mod_packed.x;  // nth i0 tile
+    const int64_t i1             = tile1;
+    const int64_t i0             = threadIdx.x + tile0 * blockDim.x;
+
+    // ne01.z is original value of unpacked ne01 (see init_fastdiv_values in common.cuh)
+    if (i0 >= ne0 || i1 >= ne01.z || i2 >= ne02 || i3 >= ne03) {
         return;
     }
 
-    const char * src0_ptr = (const char *)src0 + i3*nb03 + i2*nb02 + i1*nb01;
-    char * dst_ptr = (char *)dst + i3*nb3 + i2*nb2 + i1*nb1;
+    const char * src0_ptr = (const char *) src0 + i3 * nb03 + i2 * nb02 + i1 * nb01;
+    char *       dst_ptr  = (char *) dst + i3 * nb3 + i2 * nb2 + i1 * nb1;
 
-    for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
-        float value;
+    const int64_t rel_i0 = i0 - p0;  // relative i0 in src0
+    int64_t src_idx;
 
-        if (i0 < p0) {
-            // Left padding - reflect
-            value = *(const float *)(src0_ptr + (p0 - i0) * nb00);
-        } else if (i0 < ne0 - p1) {
-            // Middle - copy
-            value = *(const float *)(src0_ptr + (i0 - p0) * nb00);
-        } else {
-            // Right padding - reflect
-            int64_t src_idx = (ne0 - p1 - p0) - (p1 + 1 - (ne0 - i0)) - 1;
-            value = *(const float *)(src0_ptr + src_idx * nb00);
-        }
-
-        *(float *)(dst_ptr + i0 * nb0) = value;
+    if (rel_i0 < 0) {
+        // Left padding - reflect
+        src_idx = -rel_i0;
+    } else if (rel_i0 < ne00) {
+        // Middle - copy
+        src_idx = rel_i0;
+    } else {
+        // Right padding - reflect
+        src_idx = 2 * ne00 - 2 - rel_i0;
     }
+    const float value               = *(const float *) (src0_ptr + src_idx * nb00);
+    *(float *) (dst_ptr + i0 * nb0) = value;
 }
 
 void ggml_cuda_op_pad_reflect_1d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    cudaStream_t stream = ctx.stream();
+    const ggml_tensor * src0   = dst->src[0];
+    cudaStream_t        stream = ctx.stream();
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type == GGML_TYPE_F32);
 
     const int32_t * opts = (const int32_t *) dst->op_params;
-    const int p0 = opts[0];
-    const int p1 = opts[1];
+    const int       p0   = opts[0];
+    const int       p1   = opts[1];
 
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne02 = src0->ne[2];
-    const int64_t ne03 = src0->ne[3];
+    const int64_t ne00        = src0->ne[0];
+    const int64_t ne01        = src0->ne[1];
+    const uint3   ne01_packed = init_fastdiv_values(ne01);
+    const int64_t ne02        = src0->ne[2];
+    const int64_t ne03        = src0->ne[3];
 
     const int64_t ne0 = dst->ne[0];
 
+    // sanity: padded length matches
     GGML_ASSERT(ne0 == ne00 + p0 + p1);
 
-    const dim3 block_dims(CUDA_PAD_REFLECT_1D_BLOCK_SIZE, 1, 1);
-    const dim3 grid_dims(ne01, ne02, ne03);
+    constexpr int64_t bx     = CUDA_PAD_REFLECT_1D_BLOCK_SIZE;  // threads per block (x)
+    const int64_t     tiles0 = (ne0 + bx - 1) / bx;             // number of tiles along i0
+    // grid.x covers i1 and all tiles of i0: [ne01 * tiles0]
+    // grid.y covers i2: [ne02]
+    // grid.z covers i3: [ne03]
+    const dim3        grid_dims((unsigned) (ne01 * tiles0), (unsigned) ne02, (unsigned) ne03);
+    const dim3        block_dims((unsigned) bx, 1, 1);
 
     pad_reflect_1d_kernel_f32<<<grid_dims, block_dims, 0, stream>>>(
-        src0->data, dst->data,
-        ne0, ne00, ne01, ne02, ne03,
-        src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
-        dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3],
-        p0, p1
-    );
+        src0->data, dst->data, ne0, ne00, ne01_packed, ne02, ne03, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
+        dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], p0, p1);
 }

ggml/src/ggml-cuda/tsembd.cu

@@ -7,11 +7,11 @@ static __global__ void timestep_embedding_f32(const float * timesteps, float * d
     int j = threadIdx.x + blockIdx.x * blockDim.x;
     float * embed_data = (float *)((char *)dst +  i*nb1);
 
-    if (dim % 2 != 0 && j == ((dim + 1) / 2)) {
-        embed_data[dim] = 0.f;
+    int half = dim / 2;
+    if (dim % 2 != 0 && j == half) {
+        embed_data[2 * half] = 0.f;
     }
 
-    int half = dim / 2;
     if (j >= half) {
         return;
     }

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

@@ -158,33 +158,41 @@
 
 #define __CUDA_ARCH__ 1300
 
-#if defined(__gfx803__) || defined(__gfx900__) || defined(__gfx906__)
-#define GCN
-#endif
+#if defined(__gfx900__) || defined(__gfx906__)
+#define GCN5
+#endif // defined(__gfx900__) || defined(__gfx906__)
 
-#if defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx942__)
-#define CDNA // For the entire family
-#endif
+#if defined(__gfx803__)
+#define GCN4
+#endif // defined(__gfx803__)
+
+#if defined(GCN5) || defined(GCN4)
+#define GCN
+#endif // defined(GCN5) || defined(GCN4)
 
 #if defined(__gfx942__)
 #define CDNA3
-#endif
+#endif // defined(__gfx942__)
 
 #if defined(__gfx90a__)
 #define CDNA2
-#endif
+#endif // defined(__gfx90a__)
 
 #if defined(__gfx908__)
 #define CDNA1
-#endif
+#endif // defined(__gfx908__)
+
+#if defined(CDNA3) || defined(CDNA2) || defined(CDNA1)
+#define CDNA // For the entire family
+#endif // defined(CDNA3) || defined(CDNA2) || defined(CDNA1)
 
 #if defined(__GFX12__)
 #define RDNA4
-#endif
+#endif // defined(__GFX12__)
 
 #if defined(__GFX11__)
 #define RDNA3
-#endif
+#endif // defined(__GFX11__)
 
 #if defined(__gfx1030__) || defined(__gfx1031__) || defined(__gfx1032__) || defined(__gfx1033__) || \
     defined(__gfx1034__) || defined(__gfx1035__) || defined(__gfx1036__) || defined(__gfx1037__)
@@ -193,7 +201,11 @@
 
 #if defined(__gfx1010__) || defined(__gfx1012__)
 #define RDNA1
-#endif
+#endif // defined(__gfx1010__) || defined(__gfx1012__)
+
+#if defined(RDNA4) || defined(RDNA3) || defined(RDNA2) || defined(RDNA1)
+#define RDNA // For the entire family
+#endif // defined(RDNA4) || defined(RDNA3) || defined(RDNA2) || defined(RDNA1)
 
 #ifndef __has_builtin
     #define __has_builtin(x) 0

ggml/src/ggml-metal/CMakeLists.txt

@@ -5,7 +5,12 @@ find_library(METALKIT_FRAMEWORK MetalKit   REQUIRED)
 message(STATUS "Metal framework found")
 
 ggml_add_backend_library(ggml-metal
-                         ggml-metal.m
+                         ggml-metal.cpp
+                         ggml-metal-device.m
+                         ggml-metal-device.cpp
+                         ggml-metal-common.cpp
+                         ggml-metal-context.m
+                         ggml-metal-ops.cpp
                         )
 
 target_link_libraries(ggml-metal PRIVATE
@@ -18,10 +23,6 @@ if (GGML_METAL_NDEBUG)
     add_compile_definitions(GGML_METAL_NDEBUG)
 endif()
 
-if (GGML_METAL_USE_BF16)
-    add_compile_definitions(GGML_METAL_USE_BF16)
-endif()
-
 # copy metal files to bin directory
 configure_file(../ggml-common.h  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h     COPYONLY)
 configure_file(ggml-metal.metal  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal  COPYONLY)

ggml/src/ggml-metal/ggml-metal-common.cpp

@@ -0,0 +1,458 @@
+#include "ggml-metal-common.h"
+
+#include "ggml-impl.h"
+#include "ggml-backend-impl.h"
+
+#include <vector>
+
+// represents a memory range (i.e. an interval from a starting address p0 to an ending address p1 in a given buffer pb)
+// the type indicates whether it is a source range (i.e. ops read data from it) or a destination range (i.e. ops write data to it)
+struct ggml_mem_range {
+    uint64_t pb; // buffer id
+
+    uint64_t p0; // begin
+    uint64_t p1; // end
+
+    ggml_mem_range_type pt;
+};
+
+struct ggml_mem_ranges {
+    std::vector<ggml_mem_range> ranges;
+
+    int debug = 0;
+};
+
+ggml_mem_ranges_t ggml_mem_ranges_init(int debug) {
+    auto * res = new ggml_mem_ranges;
+
+    res->ranges.reserve(256);
+    res->debug = debug;
+
+    return res;
+}
+
+void ggml_mem_ranges_free(ggml_mem_ranges_t mrs) {
+    delete mrs;
+}
+
+void ggml_mem_ranges_reset(ggml_mem_ranges_t mrs) {
+    mrs->ranges.clear();
+}
+
+static bool ggml_mem_ranges_add(ggml_mem_ranges_t mrs, ggml_mem_range mr) {
+    mrs->ranges.push_back(mr);
+
+    return true;
+}
+
+static ggml_mem_range ggml_mem_range_from_tensor(const ggml_tensor * tensor, ggml_mem_range_type pt) {
+    // always use the base tensor
+    tensor = tensor->view_src ? tensor->view_src : tensor;
+
+    GGML_ASSERT(!tensor->view_src);
+
+    ggml_mem_range mr;
+
+    if (tensor->buffer) {
+        // when the tensor is allocated, use the actual memory address range in the buffer
+        //
+        // take the actual allocated size with ggml_backend_buft_get_alloc_size()
+        // this can be larger than the tensor size if the buffer type allocates extra memory
+        // ref: https://github.com/ggml-org/llama.cpp/pull/15966
+        mr = {
+            /*.pb =*/ (uint64_t) tensor->buffer,
+            /*.p0 =*/ (uint64_t) tensor->data,
+            /*.p1 =*/ (uint64_t) tensor->data + ggml_backend_buft_get_alloc_size(tensor->buffer->buft, tensor),
+            /*.pt =*/ pt,
+        };
+    } else {
+        // otherwise, the pointer address is used as an unique id of the memory ranges
+        //   that the tensor will be using when it is allocated
+        mr = {
+            /*.pb =*/ (uint64_t) tensor,
+            /*.p0 =*/ 0,    //
+            /*.p1 =*/ 1024, // [0, 1024) is a dummy range, not used
+            /*.pt =*/ pt,
+        };
+    };
+
+    return mr;
+}
+
+static ggml_mem_range ggml_mem_range_from_tensor_src(const ggml_tensor * tensor) {
+    return ggml_mem_range_from_tensor(tensor, MEM_RANGE_TYPE_SRC);
+}
+
+static ggml_mem_range ggml_mem_range_from_tensor_dst(const ggml_tensor * tensor) {
+    return ggml_mem_range_from_tensor(tensor, MEM_RANGE_TYPE_DST);
+}
+
+static bool ggml_mem_ranges_add_src(ggml_mem_ranges_t mrs, const ggml_tensor * tensor) {
+    GGML_ASSERT(tensor);
+
+    ggml_mem_range mr = ggml_mem_range_from_tensor_src(tensor);
+
+    if (mrs->debug > 2) {
+        GGML_LOG_DEBUG("%s: add src range buf=%lld, [%lld, %lld)\n", __func__, mr.pb, mr.p0, mr.p1);
+    }
+
+    return ggml_mem_ranges_add(mrs, mr);
+}
+
+static bool ggml_mem_ranges_add_dst(ggml_mem_ranges_t mrs, const ggml_tensor * tensor) {
+    GGML_ASSERT(tensor);
+
+    ggml_mem_range mr = ggml_mem_range_from_tensor_dst(tensor);
+
+    if (mrs->debug > 2) {
+        GGML_LOG_DEBUG("%s: add dst range buf=%lld, [%lld, %lld)\n", __func__, mr.pb, mr.p0, mr.p1);
+    }
+
+    return ggml_mem_ranges_add(mrs, mr);
+}
+
+bool ggml_mem_ranges_add(ggml_mem_ranges_t mrs, const ggml_tensor * tensor) {
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (tensor->src[i]) {
+            ggml_mem_ranges_add_src(mrs, tensor->src[i]);
+        }
+    }
+
+    return ggml_mem_ranges_add_dst(mrs, tensor);
+}
+
+static bool ggml_mem_ranges_check(ggml_mem_ranges_t mrs, ggml_mem_range mr) {
+    for (size_t i = 0; i < mrs->ranges.size(); i++) {
+        const auto & cmp = mrs->ranges[i];
+
+        // two memory ranges cannot intersect if they are in different buffers
+        if (mr.pb != cmp.pb) {
+            continue;
+        }
+
+        // intersecting source ranges are allowed
+        if (mr.pt == MEM_RANGE_TYPE_SRC && cmp.pt == MEM_RANGE_TYPE_SRC) {
+            continue;
+        }
+
+        if (mr.p0 < cmp.p1 && mr.p1 >= cmp.p0) {
+            if (mrs->debug > 2) {
+                GGML_LOG_DEBUG("%s: the %s range buf=%lld, [%lld, %lld) overlaps with a previous %s range buf=%lld, [%lld, %lld)\n",
+                        __func__,
+                        mr.pt == MEM_RANGE_TYPE_SRC ? "src" : "dst",
+                        mr.pb, mr.p0, mr.p1,
+                        cmp.pt == MEM_RANGE_TYPE_SRC ? "src" : "dst",
+                        cmp.pb, cmp.p0, cmp.p1);
+            }
+
+            return false;
+        }
+    }
+
+    return true;
+}
+
+static bool ggml_mem_ranges_check_src(ggml_mem_ranges_t mrs, const ggml_tensor * tensor) {
+    GGML_ASSERT(tensor);
+
+    ggml_mem_range mr = ggml_mem_range_from_tensor_src(tensor);
+
+    const bool res = ggml_mem_ranges_check(mrs, mr);
+
+    return res;
+}
+
+static bool ggml_mem_ranges_check_dst(ggml_mem_ranges_t mrs, const ggml_tensor * tensor) {
+    GGML_ASSERT(tensor);
+
+    ggml_mem_range mr = ggml_mem_range_from_tensor_dst(tensor);
+
+    const bool res = ggml_mem_ranges_check(mrs, mr);
+
+    return res;
+}
+
+bool ggml_mem_ranges_check(ggml_mem_ranges_t mrs, const ggml_tensor * tensor) {
+    for (int i = 0; i < GGML_MAX_DIMS; i++) {
+        if (tensor->src[i]) {
+            if (!ggml_mem_ranges_check_src(mrs, tensor->src[i])) {
+                return false;
+            }
+        }
+    }
+
+    return ggml_mem_ranges_check_dst(mrs, tensor);
+}
+
+// TODO: move to ggml.h?
+static bool is_empty(ggml_op op) {
+    switch (op) {
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_TRANSPOSE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+            return true;
+        default:
+            return false;
+    }
+}
+
+struct node_info {
+    ggml_tensor * node;
+
+    std::vector<ggml_tensor *> fused;
+
+    ggml_op op() const {
+        return node->op;
+    }
+
+    const ggml_tensor * dst() const {
+        return fused.empty() ? node : fused.back();
+    }
+
+    bool is_empty() const {
+        return ::is_empty(node->op);
+    }
+
+    void add_fused(ggml_tensor * t) {
+        fused.push_back(t);
+    }
+};
+
+static std::vector<int> ggml_metal_graph_optimize_reorder(const std::vector<node_info> & nodes) {
+    // helper to add node src and dst ranges
+    const auto & h_add = [](ggml_mem_ranges_t mrs, const node_info & node) {
+        for (int i = 0; i < GGML_MAX_SRC; i++) {
+            if (node.node->src[i]) {
+                if (!ggml_mem_ranges_add_src(mrs, node.node->src[i])) {
+                    return false;
+                }
+            }
+        }
+
+        // keep track of the sources of the fused nodes as well
+        for (const auto * fused : node.fused) {
+            for (int i = 0; i < GGML_MAX_SRC; i++) {
+                if (fused->src[i]) {
+                    if (!ggml_mem_ranges_add_src(mrs, fused->src[i])) {
+                        return false;
+                    }
+                }
+            }
+        }
+
+        return ggml_mem_ranges_add_dst(mrs, node.dst());
+    };
+
+    // helper to check if a node can run concurrently with the existing set of nodes
+    const auto & h_check = [](ggml_mem_ranges_t mrs, const node_info & node) {
+        for (int i = 0; i < GGML_MAX_SRC; i++) {
+            if (node.node->src[i]) {
+                if (!ggml_mem_ranges_check_src(mrs, node.node->src[i])) {
+                    return false;
+                }
+            }
+        }
+
+        for (const auto * fused : node.fused) {
+            for (int i = 0; i < GGML_MAX_SRC; i++) {
+                if (fused->src[i]) {
+                    if (!ggml_mem_ranges_check_src(mrs, fused->src[i])) {
+                        return false;
+                    }
+                }
+            }
+        }
+
+        return ggml_mem_ranges_check_dst(mrs, node.dst());
+    };
+
+    // perform reorders only across these types of ops
+    // can be expanded when needed
+    // IMPORTANT: do not add ops such as GGML_OP_CPY or GGML_OP_SET_ROWS
+    //            the dependencies from such ops are not always represented in the graph
+    const auto & h_safe = [](ggml_op op) {
+        switch (op) {
+            case GGML_OP_MUL_MAT:
+            case GGML_OP_MUL_MAT_ID:
+            case GGML_OP_ROPE:
+            case GGML_OP_NORM:
+            case GGML_OP_RMS_NORM:
+            case GGML_OP_GROUP_NORM:
+            case GGML_OP_SUM_ROWS:
+            case GGML_OP_MUL:
+            case GGML_OP_ADD:
+            case GGML_OP_DIV:
+            case GGML_OP_GLU:
+            case GGML_OP_SCALE:
+            case GGML_OP_GET_ROWS:
+                return true;
+            default:
+                return is_empty(op);
+        }
+    };
+
+    const int n = nodes.size();
+
+    std::vector<int> res;
+    res.reserve(n);
+
+    std::vector<bool> used(n, false);
+
+    // the memory ranges for the set of currently concurrent nodes
+    ggml_mem_ranges_t mrs0 = ggml_mem_ranges_init(0);
+
+    // the memory ranges for the set of nodes that haven't been processed yet, when looking forward for a node to reorder
+    ggml_mem_ranges_t mrs1 = ggml_mem_ranges_init(0);
+
+    for (int i0 = 0; i0 < n; i0++) {
+        if (used[i0]) {
+            continue;
+        }
+
+        const auto & node0 = nodes[i0];
+
+        // the node is not concurrent with the existing concurrent set, so we have to "put a barrier" (i.e reset mrs0)
+        // but before we do that, look forward for some other nodes that can be added to the concurrent set mrs0
+        //
+        // note: we can always add empty nodes to the concurrent set as they don't read nor write anything
+        if (!node0.is_empty() && !h_check(mrs0, node0)) {
+            // this will hold the set of memory ranges from the nodes that haven't been processed yet
+            // if a node is not concurrent with this set, we cannot reorder it
+            ggml_mem_ranges_reset(mrs1);
+
+            // initialize it with the current node
+            h_add(mrs1, node0);
+
+            // that many nodes forward to search for a concurrent node
+            constexpr int N_FORWARD = 8;
+
+            for (int i1 = i0 + 1; i1 < i0 + N_FORWARD && i1 < n; i1++) {
+                if (used[i1]) {
+                    continue;
+                }
+
+                const auto & node1 = nodes[i1];
+
+                // disallow reordering of certain ops
+                if (!h_safe(node1.op())) {
+                    break;
+                }
+
+                const bool is_empty = node1.is_empty();
+
+                // to reorder a node and add it to the concurrent set, it has to be:
+                //   + empty or concurrent with all nodes in the existing concurrent set (mrs0)
+                //   + concurrent with all nodes prior to it that haven't been processed yet (mrs1)
+                if ((is_empty || h_check(mrs0, node1)) && h_check(mrs1, node1)) {
+                    // add the node to the existing concurrent set (i.e. reorder it for early execution)
+                    h_add(mrs0, node1);
+                    res.push_back(i1);
+
+                    // mark as used, so we skip re-processing it later
+                    used[i1] = true;
+                } else {
+                    // expand the set of nodes that haven't been processed yet
+                    h_add(mrs1, node1);
+                }
+            }
+
+            // finalize the concurrent set and begin a new one
+            ggml_mem_ranges_reset(mrs0);
+        }
+
+        // expand the concurrent set with the current node
+        {
+            h_add(mrs0, node0);
+            res.push_back(i0);
+        }
+    }
+
+    ggml_mem_ranges_free(mrs0);
+    ggml_mem_ranges_free(mrs1);
+
+    return res;
+}
+
+void ggml_graph_optimize(ggml_cgraph * gf) {
+    constexpr int MAX_FUSE = 16;
+
+    const int n = gf->n_nodes;
+
+    enum ggml_op ops[MAX_FUSE];
+
+    std::vector<node_info> nodes;
+    nodes.reserve(gf->n_nodes);
+
+    // fuse nodes:
+    // we don't want to make reorders that break fusing, so we first pack all fusable tensors
+    //   and perform the reorder over the fused nodes. after the reorder is done, we unfuse
+    for (int i = 0; i < n; i++) {
+        node_info node = {
+            /*.node =*/ gf->nodes[i],
+            /*.fused =*/ {},
+        };
+
+        // fuse only ops that start with these operations
+        // can be expanded when needed
+        if (node.op() == GGML_OP_ADD ||
+            node.op() == GGML_OP_RMS_NORM) {
+            ops[0] = node.op();
+
+            int f = i + 1;
+            while (f < n && f < i + MAX_FUSE) {
+                // conservatively allow fusing only these ops
+                // can be expanded when needed
+                if (gf->nodes[f]->op != GGML_OP_ADD &&
+                    gf->nodes[f]->op != GGML_OP_MUL &&
+                    gf->nodes[f]->op != GGML_OP_RMS_NORM) {
+                    break;
+                }
+                ops[f - i] = gf->nodes[f]->op;
+                f++;
+            }
+
+            f -= i;
+            for (; f > 1; f--) {
+                if (ggml_can_fuse(gf, i, ops, f)) {
+                    break;
+                }
+            }
+
+            // add the fused tensors into the node info so we can unfuse them later
+            for (int k = 1; k < f; k++) {
+                ++i;
+
+                // the .dst() becomes the last fused tensor
+                node.add_fused(gf->nodes[i]);
+            }
+        }
+
+        nodes.push_back(std::move(node));
+    }
+
+#if 1
+    // reorder to improve concurrency
+    const auto order = ggml_metal_graph_optimize_reorder(nodes);
+#else
+    std::vector<int> order(nodes.size());
+    for (size_t i = 0; i < nodes.size(); i++) {
+        order[i] = i;
+    }
+#endif
+
+    // unfuse
+    {
+        int j = 0;
+        for (const auto i : order) {
+            const auto & node = nodes[i];
+
+            gf->nodes[j++] = node.node;
+
+            for (auto * fused : node.fused) {
+                gf->nodes[j++] = fused;
+            }
+        }
+    }
+}

ggml/src/ggml-metal/ggml-metal-common.h

@@ -0,0 +1,52 @@
+// helper functions for ggml-metal that are too difficult to implement in Objective-C
+
+#pragma once
+
+#include <stdbool.h>
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct ggml_tensor;
+struct ggml_cgraph;
+
+enum ggml_mem_range_type {
+    MEM_RANGE_TYPE_SRC = 0,
+    MEM_RANGE_TYPE_DST = 1,
+};
+
+// a helper object that can be used for reordering operations to improve concurrency
+//
+// the fundamental idea is that a set of tasks (either ggml ops, or something else) can run concurrently if they
+//   don't write to a memory that is being read by another task or written to by another task in the set
+//
+// with this structure, we can add tasks to the set, setting memory constraints. we can also check if a new task
+//   can be added to the set without violating the constraints (i.e. if it can be executed concurrently with the
+//   tasks already in the set)
+//
+typedef struct ggml_mem_ranges * ggml_mem_ranges_t;
+
+ggml_mem_ranges_t ggml_mem_ranges_init(int debug);
+void ggml_mem_ranges_free(ggml_mem_ranges_t mrs);
+
+// remove all ranges from the set
+void ggml_mem_ranges_reset(ggml_mem_ranges_t mrs);
+
+// add src or dst ranges to track
+bool ggml_mem_ranges_add(ggml_mem_ranges_t mrs, const struct ggml_tensor * tensor);
+
+// return false if:
+// - new src range overlaps with any existing dst range
+// - new dst range overlaps with any existing range (src or dst)
+bool ggml_mem_ranges_check(ggml_mem_ranges_t mrs, const struct ggml_tensor * tensor);
+
+// reorder the nodes in the graph to improve concurrency, while respecting fusion
+//
+// note: this implementation is generic and not specific to metal
+//       if it proves to work well, we can start using it for other backends in the future
+void ggml_graph_optimize(struct ggml_cgraph * gf);
+
+#ifdef __cplusplus
+}
+#endif

ggml/src/ggml-metal/ggml-metal-context.h

@@ -0,0 +1,33 @@
+#pragma once
+
+#include "ggml-metal-device.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+//
+// backend context
+//
+
+typedef struct ggml_metal * ggml_metal_t;
+
+ggml_metal_t ggml_metal_init(ggml_metal_device_t dev);
+void ggml_metal_free(ggml_metal_t ctx);
+
+void ggml_metal_synchronize(ggml_metal_t ctx);
+
+void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
+
+enum ggml_status ggml_metal_graph_compute (ggml_metal_t ctx, struct ggml_cgraph * gf);
+void             ggml_metal_graph_optimize(ggml_metal_t ctx, struct ggml_cgraph * gf);
+
+void ggml_metal_set_n_cb            (ggml_metal_t ctx, int n_cb);
+void ggml_metal_set_abort_callback  (ggml_metal_t ctx, ggml_abort_callback abort_callback, void * user_data);
+bool ggml_metal_supports_family     (ggml_metal_t ctx, int family);
+void ggml_metal_capture_next_compute(ggml_metal_t ctx);
+
+#ifdef __cplusplus
+}
+#endif

ggml/src/ggml-metal/ggml-metal-context.m

@@ -0,0 +1,575 @@
+#import "ggml-metal-context.h"
+
+#import "ggml-impl.h"
+#import "ggml-backend-impl.h"
+
+#import "ggml-metal-impl.h"
+#import "ggml-metal-common.h"
+#import "ggml-metal-ops.h"
+
+#import <Foundation/Foundation.h>
+
+#import <Metal/Metal.h>
+
+#undef MIN
+#undef MAX
+#define MIN(a, b) ((a) < (b) ? (a) : (b))
+#define MAX(a, b) ((a) > (b) ? (a) : (b))
+
+// max number of MTLCommandBuffer used to submit a graph for processing
+#define GGML_METAL_MAX_COMMAND_BUFFERS 8
+
+struct ggml_metal_command_buffer {
+    id<MTLCommandBuffer> obj;
+};
+
+struct ggml_metal {
+    id<MTLDevice>       device;
+    id<MTLCommandQueue> queue; // currently a pointer to the device queue, but might become separate queue [TAG_QUEUE_PER_BACKEND]
+
+    ggml_metal_device_t  dev;
+    ggml_metal_library_t lib;
+
+    dispatch_queue_t d_queue;
+
+    // additional, inference-time compiled pipelines
+    ggml_metal_pipelines_t pipelines_ext;
+
+    bool use_bfloat;
+    bool use_fusion;
+    bool use_concurrency;
+    bool use_graph_optimize;
+
+    int debug_graph;
+    int debug_fusion;
+
+    // how many times a given op was fused
+    uint64_t fuse_cnt[GGML_OP_COUNT];
+
+    // capture state
+    bool capture_next_compute;
+    bool capture_started;
+
+    id<MTLCaptureScope> capture_scope;
+
+    // command buffer state
+    int n_cb;           // number of extra threads used to submit the command buffers
+    int n_nodes_0;      // number of nodes submitted by the main thread
+    int n_nodes_1;      // remaining number of nodes submitted by the n_cb threads
+    int n_nodes_per_cb;
+
+    struct ggml_cgraph * gf;
+
+    // the callback given to the thread pool
+    void (^encode_async)(size_t ith);
+
+    // n_cb command buffers + 1 used by the main thread
+    struct ggml_metal_command_buffer cmd_bufs[GGML_METAL_MAX_COMMAND_BUFFERS + 1];
+
+    // extra command buffers for things like getting, setting and copying tensors
+    NSMutableArray * cmd_bufs_ext;
+
+    // the last command buffer queued into the Metal queue with operations relevant to the current Metal backend
+    id<MTLCommandBuffer> cmd_buf_last;
+
+    // abort ggml_metal_graph_compute if callback returns true
+    ggml_abort_callback abort_callback;
+    void *              abort_callback_data;
+};
+
+ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) {
+    GGML_LOG_INFO("%s: allocating\n", __func__);
+
+#if TARGET_OS_OSX && !GGML_METAL_NDEBUG
+    // Show all the Metal device instances in the system
+    NSArray * devices = MTLCopyAllDevices();
+    for (id<MTLDevice> device in devices) {
+        GGML_LOG_INFO("%s: found device: %s\n", __func__, [[device name] UTF8String]);
+    }
+    [devices release]; // since it was created by a *Copy* C method
+#endif
+
+    // init context
+    ggml_metal_t res = calloc(1, sizeof(struct ggml_metal));
+
+    res->device = ggml_metal_device_get_obj(dev);
+
+    GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[res->device name] UTF8String]);
+
+    // TODO: would it be better to have one queue for the backend and one queue for the device?
+    //       the graph encoders and async ops would use the backend queue while the sync ops would use the device queue?
+    //res->queue = [device newCommandQueue]; [TAG_QUEUE_PER_BACKEND]
+    res->queue = ggml_metal_device_get_queue(dev);
+    if (res->queue == nil) {
+        GGML_LOG_ERROR("%s: error: failed to create command queue\n", __func__);
+        return NULL;
+    }
+
+    res->dev = dev;
+    res->lib = ggml_metal_device_get_library(dev);
+    if (res->lib == NULL) {
+        GGML_LOG_WARN("%s: the device does not have a precompiled Metal library - this is unexpected\n", __func__);
+        GGML_LOG_WARN("%s: will try to compile it on the fly\n", __func__);
+
+        res->lib = ggml_metal_library_init(dev);
+        if (res->lib == NULL) {
+            GGML_LOG_ERROR("%s: error: failed to initialize the Metal library\n", __func__);
+
+            free(res);
+
+            return NULL;
+        }
+    }
+
+    const struct ggml_metal_device_props * props_dev = ggml_metal_device_get_props(dev);
+
+    res->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT);
+
+    res->use_bfloat      = props_dev->has_bfloat;
+    res->use_fusion      = getenv("GGML_METAL_FUSION_DISABLE") == nil;
+    res->use_concurrency = getenv("GGML_METAL_CONCURRENCY_DISABLE") == nil;
+
+    {
+        const char * val = getenv("GGML_METAL_GRAPH_DEBUG");
+        res->debug_graph = val ? atoi(val) : 0;
+    }
+
+    {
+        const char * val = getenv("GGML_METAL_FUSION_DEBUG");
+        res->debug_fusion = val ? atoi(val) : 0;
+    }
+
+    res->use_graph_optimize = true;
+
+    if (getenv("GGML_METAL_GRAPH_OPTIMIZE_DISABLE") != NULL) {
+        res->use_graph_optimize = false;
+    }
+
+    memset(res->fuse_cnt, 0, sizeof(res->fuse_cnt));
+
+    GGML_LOG_INFO("%s: use bfloat         = %s\n", __func__, res->use_bfloat         ? "true" : "false");
+    GGML_LOG_INFO("%s: use fusion         = %s\n", __func__, res->use_fusion         ? "true" : "false");
+    GGML_LOG_INFO("%s: use concurrency    = %s\n", __func__, res->use_concurrency    ? "true" : "false");
+    GGML_LOG_INFO("%s: use graph optimize = %s\n", __func__, res->use_graph_optimize ? "true" : "false");
+
+    res->capture_next_compute = false;
+    res->capture_started = false;
+    res->capture_scope = nil;
+
+    res->gf = nil;
+    res->encode_async = nil;
+    for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) {
+        res->cmd_bufs[i].obj = nil;
+    }
+
+    res->cmd_bufs_ext = [[NSMutableArray alloc] init];
+
+    res->cmd_buf_last = nil;
+
+    res->pipelines_ext = ggml_metal_pipelines_init();
+
+    return res;
+}
+
+void ggml_metal_free(ggml_metal_t ctx) {
+    GGML_LOG_INFO("%s: deallocating\n", __func__);
+
+    for (int i = 0; i < GGML_METAL_MAX_COMMAND_BUFFERS; ++i) {
+        if (ctx->cmd_bufs[i].obj) {
+            [ctx->cmd_bufs[i].obj release];
+        }
+    }
+
+    for (int i = 0; i < (int) ctx->cmd_bufs_ext.count; ++i) {
+        if (ctx->cmd_bufs_ext[i]) {
+            [ctx->cmd_bufs_ext[i] release];
+        }
+    }
+
+    [ctx->cmd_bufs_ext removeAllObjects];
+    [ctx->cmd_bufs_ext release];
+
+    if (ctx->pipelines_ext) {
+        ggml_metal_pipelines_free(ctx->pipelines_ext);
+        ctx->pipelines_ext = nil;
+    }
+
+    if (ctx->debug_fusion > 0) {
+        GGML_LOG_DEBUG("%s: fusion stats:\n", __func__);
+        for (int i = 0; i < GGML_OP_COUNT; i++) {
+            if (ctx->fuse_cnt[i] == 0) {
+                continue;
+            }
+
+            // note: cannot use ggml_log here
+            GGML_LOG_DEBUG("%s: - %s: %" PRIu64 "\n", __func__, ggml_op_name((enum ggml_op) i), ctx->fuse_cnt[i]);
+        }
+    }
+
+    Block_release(ctx->encode_async);
+
+    //[ctx->queue release]; // [TAG_QUEUE_PER_BACKEND]
+
+    dispatch_release(ctx->d_queue);
+
+    free(ctx);
+}
+
+void ggml_metal_synchronize(ggml_metal_t ctx) {
+    // wait for any backend operations to finish
+    if (ctx->cmd_buf_last) {
+        [ctx->cmd_buf_last waitUntilCompleted];
+        ctx->cmd_buf_last = nil;
+    }
+
+    // release any completed command buffers
+    if (ctx->cmd_bufs_ext.count > 0) {
+        for (size_t i = 0; i < ctx->cmd_bufs_ext.count; ++i) {
+            id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs_ext[i];
+
+            MTLCommandBufferStatus status = [cmd_buf status];
+            if (status != MTLCommandBufferStatusCompleted) {
+                GGML_LOG_ERROR("%s: error: command buffer %d failed with status %d\n", __func__, (int) i, (int) status);
+                if (status == MTLCommandBufferStatusError) {
+                    GGML_LOG_ERROR("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
+                }
+                GGML_ABORT("fatal error");
+            }
+
+            [cmd_buf release];
+        }
+
+        [ctx->cmd_bufs_ext removeAllObjects];
+    }
+}
+
+static struct ggml_metal_buffer_id ggml_metal_get_buffer_id(const struct ggml_tensor * t) {
+    if (!t) {
+        return (struct ggml_metal_buffer_id) { nil, 0 };
+    }
+
+    ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer;
+
+    return ggml_metal_buffer_get_id(buffer->context, t);
+}
+
+void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    @autoreleasepool {
+        // wrap the source data into a Metal buffer
+        id<MTLBuffer> buf_src = [ctx->device newBufferWithBytes:data
+                                                         length:size
+                                                        options:MTLResourceStorageModeShared];
+
+        struct ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(tensor);
+        if (bid_dst.metal == nil) {
+            GGML_ABORT("%s: failed to find buffer for tensor '%s'\n", __func__, tensor->name);
+        }
+
+        bid_dst.offs += offset;
+
+        // queue the copy operation into the queue of the Metal context
+        // this will be queued at the end, after any currently ongoing GPU operations
+        id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
+        id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
+
+        [encoder copyFromBuffer:buf_src
+                   sourceOffset:0
+                       toBuffer:bid_dst.metal
+              destinationOffset:bid_dst.offs
+                           size:size];
+
+        [encoder endEncoding];
+        [cmd_buf commit];
+
+        // do not wait here for completion
+        //[cmd_buf waitUntilCompleted];
+
+        // instead, remember a reference to the command buffer and wait for it later if needed
+        [ctx->cmd_bufs_ext addObject:cmd_buf];
+        ctx->cmd_buf_last = cmd_buf;
+
+        [cmd_buf retain];
+    }
+}
+
+void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    @autoreleasepool {
+        id<MTLBuffer> buf_dst = [ctx->device newBufferWithBytesNoCopy:data
+                                                               length:size
+                                                              options:MTLResourceStorageModeShared
+                                                          deallocator:nil];
+
+        struct ggml_metal_buffer_id bid_src = ggml_metal_get_buffer_id(tensor);
+        if (bid_src.metal == nil) {
+            GGML_ABORT("%s: failed to find buffer for tensor '%s'\n", __func__, tensor->name);
+        }
+
+        bid_src.offs += offset;
+
+        // queue the copy operation into the queue of the Metal context
+        // this will be queued at the end, after any currently ongoing GPU operations
+        id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
+        id<MTLBlitCommandEncoder> encoder = [cmd_buf blitCommandEncoder];
+
+        [encoder copyFromBuffer:bid_src.metal
+                   sourceOffset:bid_src.offs
+                       toBuffer:buf_dst
+              destinationOffset:0
+                           size:size];
+
+        [encoder endEncoding];
+        [cmd_buf commit];
+
+        // do not wait here for completion
+        //[cmd_buf waitUntilCompleted];
+
+        // instead, remember a reference to the command buffer and wait for it later if needed
+        [ctx->cmd_bufs_ext addObject:cmd_buf];
+        ctx->cmd_buf_last = cmd_buf;
+
+        [cmd_buf retain];
+    }
+}
+
+enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * gf) {
+    // number of nodes encoded by the main thread (empirically determined)
+    const int n_main = 64;
+
+    // number of threads in addition to the main thread
+    const int n_cb = ctx->n_cb;
+
+    // submit the ggml compute graph to the GPU by creating command buffers and encoding the ops in them
+    // the first n_nodes_0 are encoded and submitted for processing directly by the calling thread
+    // while these nodes are processing, we start n_cb threads to enqueue the rest of the nodes
+    // each thread creates it's own command buffer and enqueues the ops in parallel
+    //
+    // tests on M1 Pro and M2 Ultra using LLaMA models, show that optimal values for n_cb are 1 or 2
+
+    @autoreleasepool {
+        ctx->gf = gf;
+
+        ctx->n_nodes_0 = MIN(n_main, gf->n_nodes);
+        ctx->n_nodes_1 = gf->n_nodes - ctx->n_nodes_0;
+
+        ctx->n_nodes_per_cb = (ctx->n_nodes_1 + ctx->n_cb - 1) / ctx->n_cb;
+
+        const bool use_capture = ctx->capture_next_compute;
+        if (use_capture) {
+            ctx->capture_next_compute = false;
+
+            // make sure all previous computations have finished before starting the capture
+            if (ctx->cmd_buf_last) {
+                [ctx->cmd_buf_last waitUntilCompleted];
+                ctx->cmd_buf_last = nil;
+            }
+
+            if (!ctx->capture_started) {
+                // create capture scope
+                ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:ctx->device];
+
+                MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new];
+                descriptor.captureObject = ctx->capture_scope;
+                descriptor.destination = MTLCaptureDestinationGPUTraceDocument;
+                descriptor.outputURL = [NSURL fileURLWithPath:[NSString stringWithFormat:@"/tmp/perf-metal.gputrace"]];
+
+                NSError * error = nil;
+                if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) {
+                    GGML_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]);
+                } else {
+                    [ctx->capture_scope beginScope];
+                    ctx->capture_started = true;
+                }
+            }
+        }
+
+        // the main thread commits the first few commands immediately
+        // cmd_buf[n_cb]
+        {
+            id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
+            [cmd_buf retain];
+
+            if (ctx->cmd_bufs[n_cb].obj) {
+                [ctx->cmd_bufs[n_cb].obj release];
+            }
+            ctx->cmd_bufs[n_cb].obj = cmd_buf;
+
+            [cmd_buf enqueue];
+
+            ctx->encode_async(n_cb);
+        }
+
+        // remember the command buffer for the next iteration
+        ctx->cmd_buf_last = ctx->cmd_bufs[n_cb].obj;
+
+        // prepare the rest of the command buffers asynchronously (optional)
+        // cmd_buf[0.. n_cb)
+        for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
+            id<MTLCommandBuffer> cmd_buf = [ctx->queue commandBufferWithUnretainedReferences];
+            [cmd_buf retain];
+
+            if (ctx->cmd_bufs[cb_idx].obj) {
+                [ctx->cmd_bufs[cb_idx].obj release];
+            }
+            ctx->cmd_bufs[cb_idx].obj = cmd_buf;
+
+            // always enqueue the first two command buffers
+            // enqueue all of the command buffers if we don't need to abort
+            if (cb_idx < 2 || ctx->abort_callback == NULL) {
+                [cmd_buf enqueue];
+
+                // update the pointer to the last queued command buffer
+                // this is needed to implement synchronize()
+                ctx->cmd_buf_last = cmd_buf;
+            }
+        }
+
+        dispatch_apply(n_cb, ctx->d_queue, ctx->encode_async);
+
+        // for debugging: block until graph is computed
+        //[ctx->cmd_buf_last waitUntilCompleted];
+
+        // enter here only when capturing in order to wait for all computation to finish
+        // otherwise, we leave the graph to compute asynchronously
+        if (!use_capture && ctx->capture_started) {
+            // wait for completion and check status of each command buffer
+            // needed to detect if the device ran out-of-memory for example (#1881)
+            {
+                id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[n_cb].obj;
+                [cmd_buf waitUntilCompleted];
+
+                MTLCommandBufferStatus status = [cmd_buf status];
+                if (status != MTLCommandBufferStatusCompleted) {
+                    GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, n_cb, status);
+                    if (status == MTLCommandBufferStatusError) {
+                        GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
+                    }
+
+                    return GGML_STATUS_FAILED;
+                }
+            }
+
+            for (int i = 0; i < n_cb; ++i) {
+                id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[i].obj;
+                [cmd_buf waitUntilCompleted];
+
+                MTLCommandBufferStatus status = [cmd_buf status];
+                if (status != MTLCommandBufferStatusCompleted) {
+                    GGML_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
+                    if (status == MTLCommandBufferStatusError) {
+                        GGML_LOG_INFO("error: %s\n", [[cmd_buf error].localizedDescription UTF8String]);
+                    }
+
+                    return GGML_STATUS_FAILED;
+                }
+
+                id<MTLCommandBuffer> next_buffer = (i + 1 < n_cb ? ctx->cmd_bufs[i + 1].obj : nil);
+                if (!next_buffer) {
+                    continue;
+                }
+
+                const bool next_queued = ([next_buffer status] != MTLCommandBufferStatusNotEnqueued);
+                if (next_queued) {
+                    continue;
+                }
+
+                if (ctx->abort_callback && ctx->abort_callback(ctx->abort_callback_data)) {
+                    GGML_LOG_INFO("%s: command buffer %d aborted", __func__, i);
+                    return GGML_STATUS_ABORTED;
+                }
+
+                [next_buffer commit];
+            }
+
+            [ctx->capture_scope endScope];
+            [[MTLCaptureManager sharedCaptureManager] stopCapture];
+        }
+    }
+
+    return GGML_STATUS_SUCCESS;
+}
+
+void ggml_metal_graph_optimize(ggml_metal_t ctx, struct ggml_cgraph * gf) {
+    //const int64_t t_start = ggml_time_us();
+
+    if (ctx->use_graph_optimize) {
+        ggml_graph_optimize(gf);
+    }
+
+    //printf("%s: graph optimize took %.3f ms\n", __func__, (ggml_time_us() - t_start) / 1000.0);
+}
+
+void ggml_metal_set_n_cb(ggml_metal_t ctx, int n_cb) {
+    if (ctx->n_cb != n_cb) {
+        ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_COMMAND_BUFFERS);
+
+        if (ctx->n_cb > 2) {
+            GGML_LOG_WARN("%s: n_cb = %d, using n_cb > 2 is not recommended and can degrade the performance in some cases\n", __func__, n_cb);
+        }
+    }
+
+    if (ctx->encode_async) {
+        Block_release(ctx->encode_async);
+    }
+
+    ctx->encode_async = Block_copy(^(size_t iter) {
+        const int cb_idx = iter;
+        const int n_cb_l = ctx->n_cb;
+
+        const int n_nodes_0 = ctx->n_nodes_0;
+        const int n_nodes_1 = ctx->n_nodes_1;
+
+        const int n_nodes_per_cb = ctx->n_nodes_per_cb;
+
+        int idx_start = 0;
+        int idx_end   = n_nodes_0;
+
+        if (cb_idx < n_cb_l) {
+            idx_start = n_nodes_0 + (                                         (cb_idx + 0) * n_nodes_per_cb);
+            idx_end   = n_nodes_0 + (MIN((cb_idx == n_cb_l - 1) ? n_nodes_1 : (cb_idx + 1) * n_nodes_per_cb, n_nodes_1));
+        }
+
+        id<MTLCommandBuffer> cmd_buf = ctx->cmd_bufs[cb_idx].obj;
+
+        ggml_metal_op_t ctx_op = ggml_metal_op_init(
+            ctx->dev,
+            cmd_buf,
+            ctx->gf,
+            idx_start,
+            idx_end,
+            ctx->use_fusion,
+            ctx->use_concurrency,
+            ctx->capture_next_compute,
+            ctx->debug_graph,
+            ctx->debug_fusion);
+
+        for (int idx = idx_start; idx < idx_end;) {
+            const int res = ggml_metal_op_encode(ctx_op, idx);
+            if (res == 0) {
+                break;
+            }
+
+            idx += res;
+        }
+
+        ggml_metal_op_free(ctx_op);
+
+        if (cb_idx < 2 || ctx->abort_callback == NULL) {
+            [cmd_buf commit];
+        }
+    });
+}
+
+void ggml_metal_set_abort_callback(ggml_metal_t ctx, ggml_abort_callback abort_callback, void * user_data) {
+    ctx->abort_callback = abort_callback;
+    ctx->abort_callback_data = user_data;
+}
+
+bool ggml_metal_supports_family(ggml_metal_t ctx, int family) {
+    GGML_ASSERT(ctx->device != nil);
+
+    return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)];
+}
+
+void ggml_metal_capture_next_compute(ggml_metal_t ctx) {
+    ctx->capture_next_compute = true;
+}

ggml/src/ggml-metal/ggml-metal-device.h

@@ -0,0 +1,227 @@
+#pragma once
+
+#include "ggml.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+struct ggml_metal_buffer_id {
+    void * metal; // id<MTLBuffer>
+    size_t offs;
+};
+
+typedef struct ggml_metal_device * ggml_metal_device_t;
+
+//
+// MTLFunctionConstantValues wrapper
+//
+
+typedef struct ggml_metal_cv * ggml_metal_cv_t;
+
+ggml_metal_cv_t ggml_metal_cv_init(void);
+void ggml_metal_cv_free(ggml_metal_cv_t cv);
+
+void ggml_metal_cv_set_int16(ggml_metal_cv_t cv, int16_t value, int32_t idx);
+void ggml_metal_cv_set_int32(ggml_metal_cv_t cv, int32_t value, int32_t idx);
+void ggml_metal_cv_set_bool (ggml_metal_cv_t cv, bool    value, int32_t idx);
+
+//
+// MTLComputePipelineState wrapper
+//
+
+typedef struct ggml_metal_pipeline * ggml_metal_pipeline_t;
+
+ggml_metal_pipeline_t ggml_metal_pipeline_init(void);
+void ggml_metal_pipeline_free(ggml_metal_pipeline_t pipeline);
+
+void ggml_metal_pipeline_set_nsg(ggml_metal_pipeline_t pipeline, int nsg);
+int  ggml_metal_pipeline_get_nsg(ggml_metal_pipeline_t pipeline);
+
+void ggml_metal_pipeline_set_nr0(ggml_metal_pipeline_t pipeline, int nr0);
+int  ggml_metal_pipeline_get_nr0(ggml_metal_pipeline_t pipeline);
+
+void ggml_metal_pipeline_set_nr1(ggml_metal_pipeline_t pipeline, int nr1);
+int  ggml_metal_pipeline_get_nr1(ggml_metal_pipeline_t pipeline);
+
+void   ggml_metal_pipeline_set_smem(ggml_metal_pipeline_t pipeline, size_t smem);
+size_t ggml_metal_pipeline_get_smem(ggml_metal_pipeline_t pipeline);
+
+int ggml_metal_pipeline_max_theads_per_threadgroup(ggml_metal_pipeline_t pipeline);
+
+// a collection of pipelines
+typedef struct ggml_metal_pipelines * ggml_metal_pipelines_t;
+
+ggml_metal_pipelines_t ggml_metal_pipelines_init(void);
+void ggml_metal_pipelines_free(ggml_metal_pipelines_t ppls);
+
+void                  ggml_metal_pipelines_add(ggml_metal_pipelines_t ppls, const char * name, ggml_metal_pipeline_t pipeline);
+ggml_metal_pipeline_t ggml_metal_pipelines_get(ggml_metal_pipelines_t ppls, const char * name);
+
+//
+// MTLCommandBuffer wrapper
+//
+
+typedef void * ggml_metal_cmd_buf_t;
+
+//
+// MTLComputeCommandEncoder wrapper
+//
+
+typedef struct ggml_metal_encoder * ggml_metal_encoder_t;
+
+ggml_metal_encoder_t ggml_metal_encoder_init(ggml_metal_cmd_buf_t cmd_buf_raw, bool concurrent);
+void ggml_metal_encoder_free(ggml_metal_encoder_t encoder);
+
+void ggml_metal_encoder_debug_group_push(ggml_metal_encoder_t encoder, const char * name);
+void ggml_metal_encoder_debug_group_pop (ggml_metal_encoder_t encoder);
+
+void ggml_metal_encoder_set_pipeline(ggml_metal_encoder_t encoder, ggml_metal_pipeline_t pipeline);
+
+void ggml_metal_encoder_set_bytes (ggml_metal_encoder_t encoder, void * data, size_t size, int idx);
+void ggml_metal_encoder_set_buffer(ggml_metal_encoder_t encoder, struct ggml_metal_buffer_id buffer, int idx);
+
+void ggml_metal_encoder_set_threadgroup_memory_size(ggml_metal_encoder_t encoder, size_t size, int idx);
+
+void ggml_metal_encoder_dispatch_threadgroups(ggml_metal_encoder_t encoder, int tg0, int tg1, int tg2, int tptg0, int tptg1, int tptg2);
+
+void ggml_metal_encoder_memory_barrier(ggml_metal_encoder_t encoder);
+
+void ggml_metal_encoder_end_encoding(ggml_metal_encoder_t encoder);
+
+//
+// MTLLibrary wrapper
+//
+
+typedef struct ggml_metal_library * ggml_metal_library_t;
+
+ggml_metal_library_t ggml_metal_library_init(ggml_metal_device_t dev);
+void ggml_metal_library_free(ggml_metal_library_t lib);
+
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline    (ggml_metal_library_t lib, const char * name);
+ggml_metal_pipeline_t ggml_metal_library_compile_pipeline(ggml_metal_library_t lib, const char * base, const char * name, ggml_metal_cv_t cv);
+
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_base              (ggml_metal_library_t lib, enum ggml_op op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_cpy               (ggml_metal_library_t lib, enum ggml_type tsrc, enum ggml_type tdst);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pool_2d           (ggml_metal_library_t lib, const struct ggml_tensor * op, enum ggml_op_pool op_pool);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_get_rows          (ggml_metal_library_t lib, enum ggml_type tsrc);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_set_rows          (ggml_metal_library_t lib, enum ggml_type tdst);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_repeat            (ggml_metal_library_t lib, enum ggml_type tsrc);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_unary             (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_glu               (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_sum_rows          (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_soft_max          (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_ssm_conv          (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_ssm_scan          (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_rwkv              (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mv_ext        (ggml_metal_library_t lib, enum ggml_type tsrc0, enum ggml_type tsrc1, int nsg, int nxpsg, int r1ptg);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mm            (ggml_metal_library_t lib, enum ggml_type tsrc0, enum ggml_type tsrc1);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mv            (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mm_id_map0    (ggml_metal_library_t lib, int ne02, int ne20);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mm_id         (ggml_metal_library_t lib, enum ggml_type tsrc0, enum ggml_type tsrc1);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mv_id         (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argmax            (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argsort           (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_bin               (ggml_metal_library_t lib, enum ggml_op op, int32_t n_fuse, bool row);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_rms_norm          (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_l2_norm           (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_group_norm        (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_norm              (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_rope              (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_im2col            (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_transpose_1d (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_upscale           (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad               (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pad_reflect_1d    (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_arange            (ggml_metal_library_t lib, const struct ggml_tensor * op);
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_timestep_embedding(ggml_metal_library_t lib, const struct ggml_tensor * op);
+
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext(
+        ggml_metal_library_t lib,
+        const struct ggml_tensor * op,
+        bool    has_mask,
+        bool    has_sinks,
+        bool    has_bias,
+        bool    has_scap,
+        int32_t nsg);
+
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_vec(
+        ggml_metal_library_t lib,
+        const struct ggml_tensor * op,
+        bool    has_mask,
+        bool    has_sinks,
+        bool    has_bias,
+        bool    has_scap,
+        int32_t nsg,
+        int32_t nwg);
+
+ggml_metal_pipeline_t ggml_metal_library_get_pipeline_flash_attn_ext_vec_reduce(
+        ggml_metal_library_t lib,
+        const struct ggml_tensor * op,
+        int32_t dv,
+        int32_t nwg);
+
+//
+// device
+//
+
+struct ggml_metal_device_props {
+    char name[128];
+
+    size_t max_buffer_size;
+    size_t max_working_set_size;
+    size_t max_theadgroup_memory_size;
+
+    bool has_simdgroup_reduction;
+    bool has_simdgroup_mm;
+    bool has_unified_memory;
+    bool has_bfloat;
+    bool use_residency_sets;
+    bool use_shared_buffers;
+
+    bool supports_gpu_family_apple7;
+};
+
+ggml_metal_device_t ggml_metal_device_init(void);
+void ggml_metal_device_free(ggml_metal_device_t dev);
+
+// return a singleton that is automatically destroyed when the program exits
+ggml_metal_device_t ggml_metal_device_get(void);
+
+void * ggml_metal_device_get_obj  (ggml_metal_device_t dev); // id<MTLDevice>
+void * ggml_metal_device_get_queue(ggml_metal_device_t dev); // id<MTLCommandQueue>
+
+ggml_metal_library_t ggml_metal_device_get_library(ggml_metal_device_t dev);
+
+void ggml_metal_device_get_memory(ggml_metal_device_t dev, size_t * free, size_t * total);
+bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_tensor * op);
+
+const struct ggml_metal_device_props * ggml_metal_device_get_props(ggml_metal_device_t dev);
+
+//
+// device buffers
+//
+
+typedef struct ggml_metal_buffer * ggml_metal_buffer_t;
+
+ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, bool shared);
+ggml_metal_buffer_t ggml_metal_buffer_map (ggml_metal_device_t dev, void * ptr, size_t size, size_t max_tensor_size);
+
+void   ggml_metal_buffer_free     (ggml_metal_buffer_t buf);
+void * ggml_metal_buffer_get_base (ggml_metal_buffer_t buf);
+bool   ggml_metal_buffer_is_shared(ggml_metal_buffer_t buf);
+
+void   ggml_metal_buffer_memset_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * tensor, uint8_t value, size_t offset, size_t size);
+void   ggml_metal_buffer_set_tensor   (ggml_metal_buffer_t buf, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
+void   ggml_metal_buffer_get_tensor   (ggml_metal_buffer_t buf, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
+void   ggml_metal_buffer_clear        (ggml_metal_buffer_t buf, uint8_t value);
+
+// finds the Metal buffer that contains the tensor data on the GPU device
+// the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the
+// Metal buffer based on the host memory pointer
+//
+struct ggml_metal_buffer_id ggml_metal_buffer_get_id(ggml_metal_buffer_t buf, const struct ggml_tensor * t);
+
+#ifdef __cplusplus
+}
+#endif

ggml/src/ggml-metal/ggml-metal-impl.h

@@ -8,6 +8,9 @@
 //
 // TODO: for optimal performance, become function of the device and work size
 
+#define N_R0_F 2
+#define N_SG_F 4
+
 #define N_R0_Q4_0 4
 #define N_SG_Q4_0 2
 
@@ -32,13 +35,13 @@
 #define N_R0_Q3_K 2
 #define N_SG_Q3_K 2
 
-#define N_R0_Q4_K 4
+#define N_R0_Q4_K 2
 #define N_SG_Q4_K 2
 
 #define N_R0_Q5_K 2
 #define N_SG_Q5_K 2
 
-#define N_R0_Q6_K 1
+#define N_R0_Q6_K 2
 #define N_SG_Q6_K 2
 
 #define N_R0_IQ1_S 4
@@ -72,6 +75,7 @@
 #define FC_FLASH_ATTN_EXT              100
 #define FC_FLASH_ATTN_EXT_VEC          200
 #define FC_FLASH_ATTN_EXT_VEC_REDUCE   300
+#define FC_MUL_MV                      400
 
 // kernel argument structs
 //
@@ -165,6 +169,16 @@ typedef struct {
     uint64_t nb3;
 } ggml_metal_kargs_repeat;
 
+typedef struct {
+    float scale;
+    float bias;
+} ggml_metal_kargs_scale;
+
+typedef struct {
+    float min;
+    float max;
+} ggml_metal_kargs_clamp;
+
 typedef struct {
     int64_t  ne00;
     int64_t  ne01;
@@ -360,9 +374,6 @@ typedef struct {
     int32_t  ne1;
     int16_t  r2;
     int16_t  r3;
-    int16_t  nsg;
-    int16_t  nxpsg;
-    int16_t  r1ptg;
 } ggml_metal_kargs_mul_mv_ext;
 
 typedef struct {
@@ -453,7 +464,7 @@ typedef struct {
     uint64_t nb00;
     uint64_t nb01;
     uint64_t nb02;
-    int32_t  n_groups;
+    int32_t  ngrp;
     float    eps;
 } ggml_metal_kargs_group_norm;
 
@@ -506,14 +517,6 @@ typedef struct {
     uint64_t nb01;
     uint64_t nb02;
     uint64_t nb03;
-    int64_t  ne10;
-    int64_t  ne11;
-    int64_t  ne12;
-    int64_t  ne13;
-    uint64_t nb10;
-    uint64_t nb11;
-    uint64_t nb12;
-    uint64_t nb13;
     int64_t  ne0;
     int64_t  ne1;
     int64_t  ne2;
@@ -547,12 +550,6 @@ typedef struct {
     int32_t  n_head_log2;
 } ggml_metal_kargs_soft_max;
 
-typedef struct {
-    int64_t  ne00;
-    int64_t  ne01;
-    int      n_past;
-} ggml_metal_kargs_diag_mask_inf;
-
 typedef struct {
     int64_t  ne00;
     int64_t  ne01;
@@ -579,7 +576,7 @@ typedef struct {
     int64_t  n_group;
     int64_t  n_seq_tokens;
     int64_t  n_seqs;
-    int64_t  s_off;
+    uint64_t s_off;
     uint64_t nb01;
     uint64_t nb02;
     uint64_t nb03;
@@ -719,7 +716,12 @@ typedef struct {
     int64_t  IW;
     int64_t  OH;
     int64_t  OW;
-    int64_t  parallel_elements;
+    int64_t  np;
 } ggml_metal_kargs_pool_2d;
 
+typedef struct {
+     int64_t ne00;
+    uint64_t nb01;
+} ggml_metal_kargs_argmax;
+
 #endif // GGML_METAL_IMPL

ggml/src/ggml-metal/ggml-metal-ops.h

@@ -0,0 +1,81 @@
+#pragma once
+
+#include "ggml-metal-device.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+typedef struct ggml_metal_op * ggml_metal_op_t;
+
+ggml_metal_op_t ggml_metal_op_init(
+        ggml_metal_device_t dev,
+        ggml_metal_cmd_buf_t cmd_buf,
+        struct ggml_cgraph * gf,
+        int  idx_start,
+        int  idx_end,
+        bool use_fusion,
+        bool use_concurrency,
+        bool use_capture,
+        int  debug_graph,
+        int  debug_fusion);
+
+void ggml_metal_op_free(ggml_metal_op_t ctx);
+
+int ggml_metal_op_encode(ggml_metal_op_t ctx, int idx);
+
+//
+// available ops:
+//
+
+// tokens per expert
+size_t ggml_metal_op_mul_mat_id_extra_tpe(const struct ggml_tensor * op);
+
+// id map [n_tokens, n_expert]
+size_t ggml_metal_op_mul_mat_id_extra_ids(const struct ggml_tensor * op);
+
+// return true if we should use the FA vector kernel for this op
+bool ggml_metal_op_flash_attn_ext_use_vec(const struct ggml_tensor * op);
+
+size_t ggml_metal_op_flash_attn_ext_extra_tmp(const struct ggml_tensor * op);
+
+int ggml_metal_op_concat            (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_repeat            (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_acc               (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_scale             (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_clamp             (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_unary             (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_glu               (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_sum_rows          (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_get_rows          (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_set_rows          (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_soft_max          (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_ssm_conv          (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_ssm_scan          (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_rwkv              (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_cpy               (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_pool_2d           (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_mul_mat           (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_mul_mat_id        (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_add_id            (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_flash_attn_ext    (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_bin               (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_rms_norm          (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_l2_norm           (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_group_norm        (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_norm              (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_rope              (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_im2col            (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_conv_transpose_1d (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_upscale           (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_pad               (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_pad_reflect_1d    (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_arange            (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_timestep_embedding(ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_argmax            (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_argsort           (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_leaky_relu        (ggml_metal_op_t ctx, int idx);
+
+#ifdef __cplusplus
+}
+#endif

ggml/src/ggml-metal/ggml-metal.cpp

@@ -0,0 +1,718 @@
+#include "ggml-metal.h"
+
+#include "ggml-impl.h"
+#include "ggml-backend-impl.h"
+
+#include "ggml-metal-device.h"
+#include "ggml-metal-context.h"
+#include "ggml-metal-ops.h"
+
+// globals
+
+// initialized in ggml_backend_metal_reg
+static ggml_backend_reg    g_ggml_metal_reg;
+static ggml_backend_device g_ggml_metal_device;
+
+////////////////////////////////////////////////////////////////////////////////
+// backend interface
+////////////////////////////////////////////////////////////////////////////////
+
+// shared buffer
+
+static void ggml_backend_metal_buffer_shared_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_free(ctx);
+}
+
+static void * ggml_backend_metal_buffer_shared_get_base(ggml_backend_buffer_t buffer) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(ggml_metal_buffer_is_shared(ctx));
+
+    return ggml_metal_buffer_get_base(ctx);
+}
+
+static void ggml_backend_metal_buffer_shared_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_memset_tensor(ctx, tensor, value, offset, size);
+}
+
+static void ggml_backend_metal_buffer_shared_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_set_tensor(ctx, tensor, data, offset, size);
+}
+
+static void ggml_backend_metal_buffer_shared_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_get_tensor(ctx, tensor, data, offset, size);
+}
+
+static bool ggml_backend_metal_buffer_shared_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(ggml_metal_buffer_is_shared(ctx));
+
+    GGML_UNUSED(buffer);
+    GGML_UNUSED(src);
+    GGML_UNUSED(dst);
+
+    return false;
+}
+
+static void ggml_backend_metal_buffer_shared_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_clear(ctx, value);
+}
+
+static ggml_backend_buffer_i ggml_backend_metal_buffer_shared_i = {
+    /* .free_buffer     = */ ggml_backend_metal_buffer_shared_free_buffer,
+    /* .get_base        = */ ggml_backend_metal_buffer_shared_get_base,
+    /* .init_tensor     = */ NULL,
+    /* .memset_tensor   = */ ggml_backend_metal_buffer_shared_memset_tensor,
+    /* .set_tensor      = */ ggml_backend_metal_buffer_shared_set_tensor,
+    /* .get_tensor      = */ ggml_backend_metal_buffer_shared_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_metal_buffer_shared_cpy_tensor,
+    /* .clear           = */ ggml_backend_metal_buffer_shared_clear,
+    /* .reset           = */ NULL,
+};
+
+// private buffer
+
+static void ggml_backend_metal_buffer_private_free_buffer(ggml_backend_buffer_t buffer) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(!ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_free(ctx);
+}
+
+static void * ggml_backend_metal_buffer_private_get_base(ggml_backend_buffer_t buffer) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(!ggml_metal_buffer_is_shared(ctx));
+
+    return ggml_metal_buffer_get_base(ctx);
+}
+
+static void ggml_backend_metal_buffer_private_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(!ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_memset_tensor(ctx, tensor, value, offset, size);
+}
+
+static void ggml_backend_metal_buffer_private_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(!ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_set_tensor(ctx, tensor, data, offset, size);
+}
+
+static void ggml_backend_metal_buffer_private_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(!ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_get_tensor(ctx, tensor, data, offset, size);
+}
+
+static bool ggml_backend_metal_buffer_private_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(!ggml_metal_buffer_is_shared(ctx));
+
+    GGML_UNUSED(buffer);
+    GGML_UNUSED(src);
+    GGML_UNUSED(dst);
+
+    return false;
+}
+
+static void ggml_backend_metal_buffer_private_clear(ggml_backend_buffer_t buffer, uint8_t value) {
+    ggml_metal_buffer_t ctx = (ggml_metal_buffer_t)buffer->context;
+
+    GGML_ASSERT(!ggml_metal_buffer_is_shared(ctx));
+
+    ggml_metal_buffer_clear(ctx, value);
+}
+
+static ggml_backend_buffer_i ggml_backend_metal_buffer_private_i = {
+    /* .free_buffer     = */ ggml_backend_metal_buffer_private_free_buffer,
+    /* .get_base        = */ ggml_backend_metal_buffer_private_get_base,
+    /* .init_tensor     = */ NULL,
+    /* .memset_tensor   = */ ggml_backend_metal_buffer_private_memset_tensor,
+    /* .set_tensor      = */ ggml_backend_metal_buffer_private_set_tensor,
+    /* .get_tensor      = */ ggml_backend_metal_buffer_private_get_tensor,
+    /* .cpy_tensor      = */ ggml_backend_metal_buffer_private_cpy_tensor,
+    /* .clear           = */ ggml_backend_metal_buffer_private_clear,
+    /* .reset           = */ NULL,
+};
+
+//
+// buffer types
+//
+
+// common method for allocating shread or private Metal buffers
+static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size, bool shared) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)buft->device->context;
+    ggml_metal_buffer_t res = ggml_metal_buffer_init(ctx_dev, size, shared);
+
+    ggml_backend_buffer_i buf_i = ggml_metal_buffer_is_shared(res)
+        ? ggml_backend_metal_buffer_shared_i
+        : ggml_backend_metal_buffer_private_i;
+
+    return ggml_backend_buffer_init(buft, buf_i, res, size);
+}
+
+static size_t ggml_backend_metal_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    size_t res = ggml_nbytes(tensor);
+
+    // some operations require additional memory for fleeting data:
+    switch (tensor->op) {
+        case GGML_OP_MUL_MAT_ID:
+            {
+                res += ggml_metal_op_mul_mat_id_extra_tpe(tensor);
+                res += ggml_metal_op_mul_mat_id_extra_ids(tensor);
+            } break;
+        case GGML_OP_FLASH_ATTN_EXT:
+            {
+                if (ggml_metal_op_flash_attn_ext_use_vec(tensor)) {
+                    res += ggml_metal_op_flash_attn_ext_extra_tmp(tensor);
+                }
+            } break;
+        default:
+            break;
+    }
+
+    return res;
+
+    GGML_UNUSED(buft);
+}
+
+// default (shared) buffer type
+
+static const char * ggml_backend_metal_buffer_type_shared_get_name(ggml_backend_buffer_type_t buft) {
+    return "Metal";
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_t ggml_backend_metal_buffer_type_shared_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    return ggml_backend_metal_buffer_type_alloc_buffer(buft, size, true);
+}
+
+static size_t ggml_backend_metal_buffer_type_shared_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 32;
+
+    GGML_UNUSED(buft);
+}
+
+static size_t ggml_backend_metal_buffer_type_shared_get_max_size(ggml_backend_buffer_type_t buft) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)buft->device->context;
+
+    return ggml_metal_device_get_props(ctx_dev)->max_buffer_size;
+}
+
+static size_t ggml_backend_metal_buffer_type_shared_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    return ggml_backend_metal_buffer_type_get_alloc_size(buft, tensor);
+}
+
+static bool ggml_backend_metal_buffer_type_shared_is_host(ggml_backend_buffer_type_t buft) {
+    return false;
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_shared(void) {
+    static ggml_backend_buffer_type ggml_backend_buffer_type_metal = {
+        /* .iface = */ {
+            /* .get_name         = */ ggml_backend_metal_buffer_type_shared_get_name,
+            /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_shared_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_metal_buffer_type_shared_get_alignment,
+            /* .get_max_size     = */ ggml_backend_metal_buffer_type_shared_get_max_size,
+            /* .get_alloc_size   = */ ggml_backend_metal_buffer_type_shared_get_alloc_size,
+            /* .is_host          = */ ggml_backend_metal_buffer_type_shared_is_host,
+        },
+        /* .device  = */ &g_ggml_metal_device,
+        /* .context = */ NULL,
+    };
+
+    return &ggml_backend_buffer_type_metal;
+}
+
+// default (private) buffer type
+
+static const char * ggml_backend_metal_buffer_type_private_get_name(ggml_backend_buffer_type_t buft) {
+    return "Metal_Private";
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_t ggml_backend_metal_buffer_type_private_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    return ggml_backend_metal_buffer_type_alloc_buffer(buft, size, false);
+}
+
+static size_t ggml_backend_metal_buffer_type_private_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 32;
+
+    GGML_UNUSED(buft);
+}
+
+static size_t ggml_backend_metal_buffer_type_private_get_max_size(ggml_backend_buffer_type_t buft) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)buft->device->context;
+
+    return ggml_metal_device_get_props(ctx_dev)->max_buffer_size;
+}
+
+static size_t ggml_backend_metal_buffer_type_private_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    return ggml_backend_metal_buffer_type_get_alloc_size(buft, tensor);
+}
+
+static bool ggml_backend_metal_buffer_type_private_is_host(ggml_backend_buffer_type_t buft) {
+    return false;
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_private(void) {
+    static ggml_backend_buffer_type ggml_backend_buffer_type_metal = {
+        /* .iface = */ {
+            /* .get_name         = */ ggml_backend_metal_buffer_type_private_get_name,
+            /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_private_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_metal_buffer_type_private_get_alignment,
+            /* .get_max_size     = */ ggml_backend_metal_buffer_type_private_get_max_size,
+            /* .get_alloc_size   = */ ggml_backend_metal_buffer_type_private_get_alloc_size,
+            /* .is_host          = */ ggml_backend_metal_buffer_type_private_is_host,
+        },
+        /* .device  = */ &g_ggml_metal_device,
+        /* .context = */ NULL,
+    };
+
+    return &ggml_backend_buffer_type_metal;
+}
+
+// mapped buffer type
+
+static const char * ggml_backend_metal_buffer_type_mapped_get_name(ggml_backend_buffer_type_t buft) {
+    return "Metal_Mapped";
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_t ggml_backend_metal_buffer_type_mapped_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    // for mapped buffers, prefer shared memory
+    return ggml_backend_metal_buffer_type_alloc_buffer(buft, size, true);
+}
+
+static size_t ggml_backend_metal_buffer_type_mapped_get_alignment(ggml_backend_buffer_type_t buft) {
+    return 32;
+
+    GGML_UNUSED(buft);
+}
+
+static size_t ggml_backend_metal_buffer_type_mapped_get_max_size(ggml_backend_buffer_type_t buft) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)buft->device->context;
+
+    return ggml_metal_device_get_props(ctx_dev)->max_buffer_size;
+}
+
+static size_t ggml_backend_metal_buffer_type_mapped_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) {
+    return ggml_backend_metal_buffer_type_get_alloc_size(buft, tensor);
+}
+
+static bool ggml_backend_metal_buffer_type_mapped_is_host(ggml_backend_buffer_type_t buft) {
+    return false;
+
+    GGML_UNUSED(buft);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_metal_buffer_type_mapped(void) {
+    // note: not obvious, but this buffer type still needs to implement .alloc_buffer:
+    //       https://github.com/ggml-org/llama.cpp/pull/15832#discussion_r2333177099
+    static ggml_backend_buffer_type ggml_backend_buffer_type_mapped_metal = {
+        /* .iface = */ {
+            /* .get_name         = */ ggml_backend_metal_buffer_type_mapped_get_name,
+            /* .alloc_buffer     = */ ggml_backend_metal_buffer_type_mapped_alloc_buffer,
+            /* .get_alignment    = */ ggml_backend_metal_buffer_type_mapped_get_alignment,
+            /* .get_max_size     = */ ggml_backend_metal_buffer_type_mapped_get_max_size,
+            /* .get_alloc_size   = */ ggml_backend_metal_buffer_type_mapped_get_alloc_size,
+            /* .is_host          = */ ggml_backend_metal_buffer_type_mapped_is_host,
+        },
+        /* .device  = */ &g_ggml_metal_device,
+        /* .context = */ NULL,
+    };
+
+    return &ggml_backend_buffer_type_mapped_metal;
+}
+
+// backend
+
+static const char * ggml_backend_metal_name(ggml_backend_t backend) {
+    return "Metal";
+
+    GGML_UNUSED(backend);
+}
+
+static void ggml_backend_metal_free(ggml_backend_t backend) {
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    // wait for any ongoing async operations to finish
+    ggml_metal_synchronize(ctx);
+
+    ggml_metal_free(ctx);
+
+    free(backend);
+}
+
+static void ggml_backend_metal_synchronize(ggml_backend_t backend) {
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    ggml_metal_synchronize(ctx);
+}
+
+static void ggml_backend_metal_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    ggml_metal_set_tensor_async(ctx, tensor, data, offset, size);
+}
+
+static void ggml_backend_metal_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) {
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    ggml_metal_get_tensor_async(ctx, tensor, data, offset, size);
+}
+
+static bool ggml_backend_metal_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) {
+    return false;
+
+    GGML_UNUSED(backend_src);
+    GGML_UNUSED(backend_dst);
+    GGML_UNUSED(src);
+    GGML_UNUSED(dst);
+}
+
+static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    return ggml_metal_graph_compute(ctx, cgraph);
+}
+
+static void ggml_backend_metal_graph_optimize(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    ggml_metal_graph_optimize(ctx, cgraph);
+}
+
+static void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb) {
+    GGML_ASSERT(ggml_backend_is_metal(backend));
+
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    ggml_metal_set_n_cb(ctx, n_cb);
+
+}
+
+static ggml_backend_i ggml_backend_metal_i = {
+    /* .get_name                = */ ggml_backend_metal_name,
+    /* .free                    = */ ggml_backend_metal_free,
+    /* .set_tensor_async        = */ ggml_backend_metal_set_tensor_async,
+    /* .get_tensor_async        = */ ggml_backend_metal_get_tensor_async,
+    /* .cpy_tensor_async        = */ ggml_backend_metal_cpy_tensor_async, // only needed for multi-GPU setups
+    /* .synchronize             = */ ggml_backend_metal_synchronize,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_metal_graph_compute,
+
+    // the events API is needed only for multi-GPU setups, so likely no need to implement it for Metal
+    // in any case, these docs seem relevant if we ever decide to implement it:
+    // https://developer.apple.com/documentation/metal/mtlcommandbuffer#Synchronizing-Passes-with-Events
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .graph_optimize          = */ ggml_backend_metal_graph_optimize,
+};
+
+static ggml_guid_t ggml_backend_metal_guid(void) {
+    static ggml_guid guid = { 0x81, 0xa1, 0x8b, 0x1e, 0x71, 0xec, 0x79, 0xed, 0x2b, 0x85, 0xdc, 0x8a, 0x61, 0x98, 0x30, 0xe6 };
+    return &guid;
+}
+
+ggml_backend_t ggml_backend_metal_init(void) {
+    ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_metal_reg(), 0);
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    ggml_metal_t ctx = ggml_metal_init(ctx_dev);
+    if (ctx == NULL) {
+        GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
+        return NULL;
+    }
+
+    ggml_backend_t backend = (ggml_backend_t) malloc(sizeof(ggml_backend));
+
+    *backend = {
+        /* .guid      = */ ggml_backend_metal_guid(),
+        /* .interface = */ ggml_backend_metal_i,
+        /* .device    = */ dev,
+        /* .context   = */ ctx,
+    };
+
+    ggml_backend_metal_set_n_cb(backend, 1);
+
+    return backend;
+}
+
+bool ggml_backend_is_metal(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_metal_guid());
+}
+
+void ggml_backend_metal_set_abort_callback(ggml_backend_t backend, ggml_abort_callback abort_callback, void * user_data) {
+    GGML_ASSERT(ggml_backend_is_metal(backend));
+
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    ggml_metal_set_abort_callback(ctx, abort_callback, user_data);
+}
+
+bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) {
+    GGML_ASSERT(ggml_backend_is_metal(backend));
+
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    return ggml_metal_supports_family(ctx, family);
+}
+
+void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) {
+    GGML_ASSERT(ggml_backend_is_metal(backend));
+
+    ggml_metal_t ctx = (ggml_metal_t)backend->context;
+
+    ggml_metal_capture_next_compute(ctx);
+}
+
+// backend device
+
+static const char * ggml_backend_metal_device_get_name(ggml_backend_dev_t dev) {
+    return "Metal";
+
+    GGML_UNUSED(dev);
+}
+
+static const char * ggml_backend_metal_device_get_description(ggml_backend_dev_t dev) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    return ggml_metal_device_get_props(ctx_dev)->name;
+}
+
+static void ggml_backend_metal_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    ggml_metal_device_get_memory(ctx_dev, free, total);
+}
+
+static enum ggml_backend_dev_type ggml_backend_metal_device_get_type(ggml_backend_dev_t dev) {
+    return GGML_BACKEND_DEVICE_TYPE_GPU;
+
+    GGML_UNUSED(dev);
+}
+
+static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) {
+    props->name        = ggml_backend_metal_device_get_name(dev);
+    props->description = ggml_backend_metal_device_get_description(dev);
+    props->type        = ggml_backend_metal_device_get_type(dev);
+
+    ggml_backend_metal_device_get_memory(dev, &props->memory_free, &props->memory_total);
+
+    props->caps = {
+        /* .async                 = */ true,
+        /* .host_buffer           = */ false,
+        /* .buffer_from_host_ptr  = */ true,
+        /* .events                = */ false,
+    };
+}
+
+static ggml_backend_t ggml_backend_metal_device_init(ggml_backend_dev_t dev, const char * params) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    ggml_metal_t ctx = ggml_metal_init(ctx_dev);
+    if (ctx == NULL) {
+        GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__);
+        return NULL;
+    }
+
+    ggml_backend_t backend = (ggml_backend_t) malloc(sizeof(ggml_backend));
+
+    *backend = {
+        /* .guid      = */ ggml_backend_metal_guid(),
+        /* .interface = */ ggml_backend_metal_i,
+        /* .device    = */ dev,
+        /* .context   = */ ctx,
+    };
+
+    ggml_backend_metal_set_n_cb(backend, 1);
+
+    return backend;
+
+    GGML_UNUSED(params);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_metal_device_get_buffer_type(ggml_backend_dev_t dev) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    const ggml_metal_device_props * props_dev = ggml_metal_device_get_props(ctx_dev);
+
+    return props_dev->use_shared_buffers ? ggml_backend_metal_buffer_type_shared() : ggml_backend_metal_buffer_type_private();
+}
+
+static ggml_backend_buffer_t ggml_backend_metal_device_buffer_mapped(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    ggml_metal_buffer_t res = ggml_metal_buffer_map(ctx_dev, ptr, size, max_tensor_size);
+
+    return ggml_backend_buffer_init(ggml_backend_metal_buffer_type_mapped(), ggml_backend_metal_buffer_shared_i, res, size);
+}
+
+static bool ggml_backend_metal_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+    ggml_metal_device_t ctx_dev = (ggml_metal_device_t)dev->context;
+
+    return ggml_metal_device_supports_op(ctx_dev, op);
+}
+
+static bool ggml_backend_metal_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
+    return
+        buft->iface.get_name == ggml_backend_metal_buffer_type_shared_get_name ||
+        buft->iface.get_name == ggml_backend_metal_buffer_type_private_get_name ||
+        buft->iface.get_name == ggml_backend_metal_buffer_type_mapped_get_name;
+
+    GGML_UNUSED(dev);
+}
+
+static int64_t get_op_batch_size(const ggml_tensor * op) {
+    switch (op->op) {
+        case GGML_OP_MUL_MAT:
+            return op->ne[1];
+        case GGML_OP_MUL_MAT_ID:
+            return op->ne[2];
+        default:
+            return ggml_nrows(op);
+    }
+}
+
+static bool ggml_backend_metal_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+    const int min_batch_size = 32;
+
+    return (op->op == GGML_OP_MUL_MAT ||
+            op->op == GGML_OP_MUL_MAT_ID) &&
+            get_op_batch_size(op) >= min_batch_size;
+
+    GGML_UNUSED(dev);
+    GGML_UNUSED(op);
+}
+
+static ggml_backend_device_i ggml_backend_metal_device_i = {
+    /* .get_name             = */ ggml_backend_metal_device_get_name,
+    /* .get_description      = */ ggml_backend_metal_device_get_description,
+    /* .get_memory           = */ ggml_backend_metal_device_get_memory,
+    /* .get_type             = */ ggml_backend_metal_device_get_type,
+    /* .get_props            = */ ggml_backend_metal_device_get_props,
+    /* .init_backend         = */ ggml_backend_metal_device_init,
+    /* .get_buffer_type      = */ ggml_backend_metal_device_get_buffer_type,
+    /* .get_host_buffer_type = */ NULL,
+    /* .buffer_from_host_ptr = */ ggml_backend_metal_device_buffer_mapped,
+    /* .supports_op          = */ ggml_backend_metal_device_supports_op,
+    /* .supports_buft        = */ ggml_backend_metal_device_supports_buft,
+    /* .offload_op           = */ ggml_backend_metal_device_offload_op,
+    /* .event_new            = */ NULL,
+    /* .event_free           = */ NULL,
+    /* .event_synchronize    = */ NULL,
+};
+
+// backend registry
+
+static const char * ggml_backend_metal_reg_get_name(ggml_backend_reg_t reg) {
+    return "Metal";
+
+    GGML_UNUSED(reg);
+}
+
+static size_t ggml_backend_metal_reg_device_count(ggml_backend_reg_t reg) {
+    return 1;
+
+    GGML_UNUSED(reg);
+}
+
+static ggml_backend_dev_t ggml_backend_metal_reg_device_get(ggml_backend_reg_t reg, size_t index) {
+    GGML_ASSERT(index == 0);
+
+    return &g_ggml_metal_device;
+
+    GGML_UNUSED(reg);
+    GGML_UNUSED(index);
+}
+
+static ggml_backend_feature g_ggml_backend_metal_features[] = {
+#if defined(GGML_METAL_EMBED_LIBRARY)
+    { "EMBED_LIBRARY", "1" },
+#endif
+    { NULL, NULL },
+};
+
+static ggml_backend_feature * ggml_backend_metal_get_features(ggml_backend_reg_t reg) {
+    return g_ggml_backend_metal_features;
+
+    GGML_UNUSED(reg);
+}
+
+static void * ggml_backend_metal_get_proc_address(ggml_backend_reg_t reg, const char * name) {
+    if (strcmp(name, "ggml_backend_get_features") == 0) {
+        return (void *)ggml_backend_metal_get_features;
+    }
+
+    return NULL;
+
+    GGML_UNUSED(reg);
+}
+
+static ggml_backend_reg_i ggml_backend_metal_reg_i = {
+    /* .get_name         = */ ggml_backend_metal_reg_get_name,
+    /* .device_count     = */ ggml_backend_metal_reg_device_count,
+    /* .device_get       = */ ggml_backend_metal_reg_device_get,
+    /* .get_proc_address = */ ggml_backend_metal_get_proc_address,
+};
+
+ggml_backend_reg_t ggml_backend_metal_reg(void) {
+    {
+        g_ggml_metal_reg = {
+            /* .api_version = */ GGML_BACKEND_API_VERSION,
+            /* .iface       = */ ggml_backend_metal_reg_i,
+            /* .context     = */ NULL,
+        };
+
+        g_ggml_metal_device = {
+            /* .iface   = */ ggml_backend_metal_device_i,
+            /* .reg     = */ &g_ggml_metal_reg,
+            /* .context = */ ggml_metal_device_get(),
+        };
+    }
+
+    return &g_ggml_metal_reg;
+}
+
+GGML_BACKEND_DL_IMPL(ggml_backend_metal_reg)

ggml/src/ggml-opencl/CMakeLists.txt

@@ -83,8 +83,10 @@ set(GGML_OPENCL_KERNELS
     mul_mv_q4_0_f32_1d_16x_flat
     mul_mv_q6_k
     mul_mv_mxfp4_f32
+    mul_mv_mxfp4_f32_flat
     mul_mv_id_q4_0_f32_8x_flat
     mul_mv_id_mxfp4_f32
+    mul_mv_id_mxfp4_f32_flat
     mul_mm_f32_f32_l4_lm
     mul_mm_f16_f32_l4_lm
     mul

ggml/src/ggml-opencl/ggml-opencl.cpp

@@ -368,6 +368,7 @@ struct ggml_backend_opencl_context {
     cl_program program_mul_mv_q4_0_f32_1d_16x_flat;
     cl_program program_mul_mv_q6_K;
     cl_program program_mul_mv_mxfp4_f32;
+    cl_program program_mul_mv_mxfp4_f32_flat;
     cl_program program_mul_mv_f16_f16;
     cl_program program_mul_mv_f16_f32_1row;
     cl_program program_mul_mv_f16_f32_l4;
@@ -402,6 +403,7 @@ struct ggml_backend_opencl_context {
     cl_program program_tsembd;
     cl_program program_mul_mv_id_q4_0_f32_8x_flat;
     cl_program program_mul_mv_id_mxfp4_f32;
+    cl_program program_mul_mv_id_mxfp4_f32_flat;
     cl_program program_mul_mm_f32_f32_l4_lm;
     cl_program program_mul_mm_f16_f32_l4_lm;
 
@@ -447,11 +449,12 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_mul_mat_f16_f32_tiled;
     cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v;
     cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0;
+    cl_kernel kernel_convert_block_mxfp4, kernel_restore_block_mxfp4;
     cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
     cl_kernel kernel_convert_block_q4_0_noshuffle;
     cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
     cl_kernel kernel_mul_mv_q6_K_f32;
-    cl_kernel kernel_mul_mv_mxfp4_f32;
+    cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat;
     cl_kernel kernel_im2col_f32, kernel_im2col_f16;
     cl_kernel kernel_argsort_f32_i32;
     cl_kernel kernel_sum_rows_f32;
@@ -469,6 +472,7 @@ struct ggml_backend_opencl_context {
     cl_kernel kernel_timestep_embedding;
     cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat;
     cl_kernel kernel_mul_mv_id_mxfp4_f32;
+    cl_kernel kernel_mul_mv_id_mxfp4_f32_flat;
     cl_kernel kernel_mul_mm_f32_f32_l4_lm;
     cl_kernel kernel_mul_mm_f16_f32_l4_lm;
 
@@ -765,6 +769,8 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         CL_CHECK((backend_ctx->kernel_convert_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0_noshuffle", &err), err));
         CL_CHECK((backend_ctx->kernel_convert_block_q4_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err));
         CL_CHECK((backend_ctx->kernel_restore_block_q4_0  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err));
+        CL_CHECK((backend_ctx->kernel_convert_block_mxfp4  = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err));
+        CL_CHECK((backend_ctx->kernel_restore_block_mxfp4  = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4", &err), err));
         GGML_LOG_CONT(".");
     }
 
@@ -1002,6 +1008,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mv_mxfp4_f32_flat
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_mxfp4_f32_flat.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_mxfp4_f32_flat.cl");
+#endif
+        backend_ctx->program_mul_mv_mxfp4_f32_flat =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_mxfp4_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_mxfp4_f32_flat, "kernel_mul_mv_mxfp4_f32_flat", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
     // mul_mv_f16_f16
     {
 #ifdef GGML_OPENCL_EMBED_KERNELS
@@ -1727,6 +1749,22 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
         GGML_LOG_CONT(".");
     }
 
+    // mul_mv_id_mxfp4_f32_flat
+    {
+#ifdef GGML_OPENCL_EMBED_KERNELS
+        const std::string kernel_src {
+            #include "mul_mv_id_mxfp4_f32_flat.cl.h"
+        };
+#else
+        const std::string kernel_src = read_file("mul_mv_id_mxfp4_f32_flat.cl");
+#endif
+        backend_ctx->program_mul_mv_id_mxfp4_f32_flat =
+            build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
+
+        CL_CHECK((backend_ctx->kernel_mul_mv_id_mxfp4_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_id_mxfp4_f32_flat, "kernel_mul_mv_id_mxfp4_f32_flat", &err), err));
+        GGML_LOG_CONT(".");
+    }
+
     // Adreno kernels
 #ifdef GGML_OPENCL_USE_ADRENO_KERNELS
     // transpose
@@ -2391,6 +2429,51 @@ struct ggml_tensor_extra_cl_q4_0 {
     }
 };
 
+struct ggml_tensor_extra_cl_mxfp4 {
+    // Quantized values.
+    cl_mem q = nullptr;
+    // Quantized values in image1d_buffer_t.
+    cl_mem q_img = nullptr;
+    // Scales in E8M0.
+    cl_mem e = nullptr;
+    // Scales in image1d_buffer_t.
+    cl_mem e_img = nullptr;
+    // Size of quantized values.
+    size_t size_q = 0;
+    // Size of scales.
+    size_t size_e = 0;
+
+    ~ggml_tensor_extra_cl_mxfp4() {
+        reset();
+    }
+
+    void reset() {
+        // q and d are subbuffers into the bigger buffer allocated in ggml_backend_buffer.
+        // They must be properly released so that the original buffer can be
+        // properly released to avoid memory leak.
+        if (q != nullptr) {
+            CL_CHECK(clReleaseMemObject(q));
+            q = nullptr;
+        }
+        if (e != nullptr) {
+            CL_CHECK(clReleaseMemObject(e));
+            e = nullptr;
+        }
+        if (q != nullptr) {
+            CL_CHECK(clReleaseMemObject(q_img));
+            q = nullptr;
+        }
+        // Currently, q_img and d_img are only initialized when SMALL_ALLOC is
+        // enabled. They point to the images in ggml_backend_opencl_buffer_context.
+        // So, there is no need to release them here.
+        // TODO: initialize them for non SMALL_PATH path, or remove them.
+        q_img = nullptr;
+        e_img = nullptr;
+        size_q = 0;
+        size_e = 0;
+    }
+};
+
 //------------------------------------------------------------------------------
 // Backend API
 //------------------------------------------------------------------------------
@@ -2838,7 +2921,7 @@ static ggml_backend_i ggml_backend_opencl_i = {
     /* .graph_compute           = */ ggml_backend_opencl_graph_compute,
     /* .event_record            = */ NULL,
     /* .event_wait              = */ NULL,
-    /* .optimize_graph          = */ NULL,
+    /* .graph_optimize          = */ NULL,
 };
 
 ggml_backend_t ggml_backend_opencl_init(void) {
@@ -2894,6 +2977,12 @@ struct ggml_backend_opencl_buffer_context {
         for (ggml_tensor_extra_cl_q4_0 * e : temp_tensor_extras_q4_0_in_use) {
             delete e;
         }
+        for (ggml_tensor_extra_cl_mxfp4 * e : temp_tensor_extras_mxfp4) {
+            delete e;
+        }
+        for (ggml_tensor_extra_cl_mxfp4 * e : temp_tensor_extras_mxfp4_in_use) {
+            delete e;
+        }
     }
 
     ggml_tensor_extra_cl * ggml_opencl_alloc_temp_tensor_extra() {
@@ -2926,6 +3015,21 @@ struct ggml_backend_opencl_buffer_context {
         return extra;
     }
 
+    ggml_tensor_extra_cl_mxfp4 * ggml_opencl_alloc_temp_tensor_extra_mxfp4() {
+        ggml_tensor_extra_cl_mxfp4 * extra;
+        if (temp_tensor_extras_mxfp4.empty()) {
+            extra = new ggml_tensor_extra_cl_mxfp4();
+        } else {
+            extra = temp_tensor_extras_mxfp4.back();
+            temp_tensor_extras_mxfp4.pop_back();
+        }
+
+        temp_tensor_extras_mxfp4_in_use.push_back(extra);
+
+        extra->reset();
+        return extra;
+    }
+
     void reset() {
         for (ggml_tensor_extra_cl * e : temp_tensor_extras_in_use) {
             temp_tensor_extras.push_back(e);
@@ -2936,6 +3040,11 @@ struct ggml_backend_opencl_buffer_context {
             temp_tensor_extras_q4_0.push_back(e);
         }
         temp_tensor_extras_q4_0_in_use.clear();
+
+        for (ggml_tensor_extra_cl_mxfp4 * e : temp_tensor_extras_mxfp4_in_use) {
+            temp_tensor_extras_mxfp4.push_back(e);
+        }
+        temp_tensor_extras_mxfp4_in_use.clear();
     }
 
     // Pools for extras. Available extras are in `temp_tensor_extras`. Extras
@@ -2947,6 +3056,8 @@ struct ggml_backend_opencl_buffer_context {
     std::vector<ggml_tensor_extra_cl *> temp_tensor_extras_in_use;
     std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0;
     std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0_in_use;
+    std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4;
+    std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4_in_use;
 
     // The buffer_context is initially created by ggml_backend_buft_alloc_buffer
     // before any tensor is initialized (at the beginning of alloc_tensor_range).
@@ -3289,6 +3400,76 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
         }
     #endif // GGML_OPENCL_USE_ADRENO_KERNELS
 
+        return;
+
+    }
+    if (tensor->type == GGML_TYPE_MXFP4) {
+        ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
+        GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
+
+        // Allocate the new extra and create aliases from the original.
+        ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
+        ggml_tensor_extra_cl_mxfp4 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_mxfp4();
+
+        size_t size_e = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(char);
+        size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*ggml_blck_size(tensor->type)/2;
+        GGML_ASSERT(size_e + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+        CL_CHECK(clEnqueueWriteBuffer(
+            queue, data_device, CL_TRUE, 0,
+            ggml_nbytes(tensor), data, 0, NULL, NULL));
+
+        // The original tensor memory is divided into scales and quants, i.e.,
+        // we first store scales, then quants.
+        cl_buffer_region region;
+
+        // Create subbuffer for scales.
+        region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
+        region.size = size_e;
+        extra->e = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+        auto previous_origin = region.origin;
+
+        // Create subbuffer for quants.
+        region.origin = align_to(previous_origin + size_e, backend_ctx->alignment);
+        region.size = size_q;
+        extra->q = clCreateSubBuffer(
+            extra_orig->data_device, CL_MEM_READ_WRITE,
+            CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel = backend_ctx->kernel_convert_block_mxfp4;
+
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->e));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {64, 1, 1};
+
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clReleaseMemObject(data_device));
+
+        // Create image for Q
+        cl_image_format img_format_q = {CL_RG, CL_UNSIGNED_INT32};
+        cl_image_desc img_desc_q = {
+            CL_MEM_OBJECT_IMAGE1D_BUFFER,
+            static_cast<size_t>(ggml_nelements(tensor)/32*2),
+            0, 0, 0, 0, 0, 0, 0,
+            { extra->q }
+        };
+        extra->q_img = clCreateImage(context, CL_MEM_READ_ONLY, &img_format_q, &img_desc_q, NULL, &err);
+
+        tensor->extra = extra;
+
         return;
     }
 #endif // GGML_OPENCL_SOA_Q
@@ -3337,6 +3518,31 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
         size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
         size_t local_work_size[] = {1, 1, 1};
 
+        cl_event evt;
+        CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
+            global_work_size, local_work_size, 0, NULL, &evt));
+        CL_CHECK(clWaitForEvents(1, &evt));
+        CL_CHECK(clEnqueueReadBuffer(
+            queue, data_device, CL_TRUE, offset,
+            size, data, 0, NULL, NULL));
+        CL_CHECK(clReleaseMemObject(data_device));
+        return;
+    } else if (tensor->type == GGML_TYPE_MXFP4) {
+        ggml_tensor_extra_cl_mxfp4 * extra = (ggml_tensor_extra_cl_mxfp4 *)tensor->extra;
+
+        cl_int err;
+        cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
+            ggml_nbytes(tensor), NULL, &err);
+        CL_CHECK(err);
+
+        cl_kernel kernel = backend_ctx->kernel_restore_block_mxfp4;
+        CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
+        CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->e));
+        CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device));
+
+        size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
+        size_t local_work_size[] = {1, 1, 1};
+
         cl_event evt;
         CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
             global_work_size, local_work_size, 0, NULL, &evt));
@@ -3658,6 +3864,19 @@ static void dump_tensor(ggml_backend_t backend, const struct ggml_tensor * tenso
         CL_CHECK(clEnqueueReadBuffer(queue, extra->q, CL_TRUE, 0, size_q, buf_q, 0, NULL, NULL));
         CL_CHECK(clEnqueueReadBuffer(queue, extra->d, CL_TRUE, 0, size_d, buf_d, 0, NULL, NULL));
         CL_CHECK(clFinish(queue));
+    } else if (tensor->type == GGML_TYPE_MXFP4) {
+        ggml_tensor_extra_cl_mxfp4 * extra = (ggml_tensor_extra_cl_mxfp4 *) tensor->extra;
+        GGML_ASSERT(extra);
+
+        size_t size_q = ggml_nelements(tensor)/QK_MXFP4 * QK_MXFP4/2;
+        size_t size_e = ggml_nelements(tensor)/QK_MXFP4 * sizeof(char);
+        GGML_ASSERT(size_q + size_e == ggml_nbytes(tensor));
+        buf_q = malloc(size_q);
+        buf_d = malloc(size_e);
+
+        CL_CHECK(clEnqueueReadBuffer(queue, extra->q, CL_TRUE, 0, size_q, buf_q, 0, NULL, NULL));
+        CL_CHECK(clEnqueueReadBuffer(queue, extra->d, CL_TRUE, 0, size_e, buf_d, 0, NULL, NULL));
+        CL_CHECK(clFinish(queue));
     } else {
         // Read out the tensor from GPU memory.
         ggml_tensor_extra_cl * extra = (ggml_tensor_extra_cl *) tensor->extra;
@@ -6048,6 +6267,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
 
 #ifdef GGML_OPENCL_SOA_Q
     ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
+    ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
 #endif
 
     const int  ne00 = src0 ? src0->ne[0] : 0;
@@ -6752,6 +6972,45 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &r3));
             break;
         case GGML_TYPE_MXFP4: {
+#ifdef GGML_OPENCL_SOA_Q
+            kernel = backend_ctx->kernel_mul_mv_mxfp4_f32_flat;
+
+            cl_mem q;
+            if (backend_ctx->gpu_family == INTEL) {
+                nth0 = 16;
+                nth1 = 2;
+                ndst = nth1*2;
+
+                q = extra0_mxfp4->q;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                nth0 = 64;
+                nth1 = 2;
+                ndst = nth1*2;
+
+                q = extra0_mxfp4->q_img;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &q));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_mxfp4->e));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r3));
+#else
             kernel = backend_ctx->kernel_mul_mv_mxfp4_f32;
 
             if (backend_ctx->gpu_family == INTEL) {
@@ -6785,6 +7044,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
             CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int),      &r2));
             CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &r3));
             CL_CHECK(clSetKernelArg(kernel, 18, sizeof(float)*nth0,nullptr));
+#endif
             break;
         }
         default:
@@ -6850,8 +7110,11 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
     cl_ulong offset2 = extra2->offset + src2->view_offs;
     cl_ulong offsetd = extrad->offset + dst->view_offs;
 
+    GGML_UNUSED(offset0);
+
 #ifdef GGML_OPENCL_SOA_Q
     ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
+    ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
 #endif
 
     const int ne00 = src0->ne[0];
@@ -6940,6 +7203,51 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
             break;
         }
         case GGML_TYPE_MXFP4: {
+#ifdef GGML_OPENCL_SOA_Q
+            kernel = backend_ctx->kernel_mul_mv_id_mxfp4_f32_flat;
+
+            cl_mem q;
+            if (backend_ctx->gpu_family == INTEL) {
+                sgs  = 16;
+                nsg  = 2;
+                ndst = 2;
+
+                q = extra0_mxfp4->q;
+            } else if (backend_ctx->gpu_family == ADRENO) {
+                sgs  = 64;
+                nsg  = 1;
+                ndst = 4;
+
+                q = extra0_mxfp4->q_img;
+            } else {
+                GGML_ASSERT(false && "TODO: Unknown GPU");
+            }
+
+            CL_CHECK(clSetKernelArg(kernel,  0, sizeof(cl_mem),   &q));
+            CL_CHECK(clSetKernelArg(kernel,  1, sizeof(cl_mem),   &extra0_mxfp4->e));
+            CL_CHECK(clSetKernelArg(kernel,  2, sizeof(cl_mem),   &extra1->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  3, sizeof(cl_ulong), &offset1));
+            CL_CHECK(clSetKernelArg(kernel,  4, sizeof(cl_mem),   &extra2->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  5, sizeof(cl_ulong), &offset2));
+            CL_CHECK(clSetKernelArg(kernel,  6, sizeof(cl_mem),   &extrad->data_device));
+            CL_CHECK(clSetKernelArg(kernel,  7, sizeof(cl_ulong), &offsetd));
+            CL_CHECK(clSetKernelArg(kernel,  8, sizeof(int),      &ne00));
+            CL_CHECK(clSetKernelArg(kernel,  9, sizeof(cl_ulong), &nb01));
+            CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb02));
+            CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb03));
+            CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int),      &ne11));
+            CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int),      &ne12));
+            CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb11));
+            CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb12));
+            CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb13));
+            CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int),      &ne20));
+            CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int),      &ne21));
+            CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_ulong), &nb21));
+            CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int),      &ne0));
+            CL_CHECK(clSetKernelArg(kernel, 21, sizeof(int),      &ne1));
+            CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &r2));
+            CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &r3));
+#else // GGML_OPENCL_SOA_Q
             kernel = backend_ctx->kernel_mul_mv_id_mxfp4_f32;
 
             if (backend_ctx->gpu_family == INTEL) {
@@ -6979,7 +7287,7 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
             CL_CHECK(clSetKernelArg(kernel, 22, sizeof(int),      &r2));
             CL_CHECK(clSetKernelArg(kernel, 23, sizeof(int),      &r3));
             CL_CHECK(clSetKernelArg(kernel, 24, sizeof(float)*sgs,nullptr));
-
+#endif // GGML_OPENCL_SOA_Q
             break;
         }
         default:

ggml/src/ggml-opencl/kernels/cvt.cl

@@ -116,3 +116,49 @@ kernel void kernel_convert_block_q4_0_noshuffle(
 #endif
     }
 }
+
+
+//------------------------------------------------------------------------------
+// block_q4_0
+//------------------------------------------------------------------------------
+#define QK_MXFP4 32
+struct block_mxfp4 {
+    uchar e; // E8M0
+    uchar qs[QK_MXFP4 / 2];
+};
+
+//------------------------------------------------------------------------------
+// kernel_convert_block_mxfp4
+// Convert the block_mxfp4 format to 2 separate arrays (AOS -> SOA).
+// This kernel does not deshuffle the bits.
+//------------------------------------------------------------------------------
+kernel void kernel_convert_block_mxfp4(
+    global struct block_mxfp4 * src0,
+    global uchar * dst_q,
+    global uchar * dst_e
+) {
+    global struct block_mxfp4 * b = (global struct block_mxfp4 *) src0 + get_global_id(0);
+    global uchar * q = (global uchar *) dst_q + QK_MXFP4 / 2 * get_global_id(0);
+    global uchar * e = (global uchar *) dst_e + get_global_id(0);
+
+    *e = b->e;
+
+    for (int i = 0; i < QK_MXFP4 / 2; ++i) {
+        q[i] = b->qs[i];
+    }
+}
+
+kernel void kernel_restore_block_mxfp4(
+    global uchar * src_q,
+    global half  * src_e,
+    global struct block_mxfp4 * dst
+) {
+    global struct block_mxfp4 * b = (global struct block_mxfp4 *) dst + get_global_id(0);
+    global uchar * q = (global uchar *) src_q + QK_MXFP4 / 2 * get_global_id(0);
+    global uchar * e = (global uchar *) src_e + get_global_id(0);
+
+    b->e = *e;
+    for (int i = 0; i < QK_MXFP4 / 2; ++i) {
+        b->qs[i] = q[i];
+    }
+}

ggml/src/ggml-opencl/kernels/mul_mv_id_mxfp4_f32_flat.cl

@@ -0,0 +1,176 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK_MXFP4 32
+
+static inline half4 mxfp4_to_fp16_packed(ushort fp4x4) {
+    ushort2 fp16_packed_a, fp16_packed_b, bias_a, bias_b, sign_a, sign_b;
+    fp16_packed_a.lo = (fp4x4 << 9) & 0x0E00;
+    fp16_packed_a.hi = (fp4x4 << 5) & 0x0E00;
+    fp16_packed_b.lo = (fp4x4 << 1) & 0x0E00;
+    fp16_packed_b.hi = (fp4x4 >> 3) & 0x0E00;
+
+    bias_a.lo = (fp16_packed_a.lo == 0) ? 0x0 : 0x3800;
+    bias_a.hi = (fp16_packed_a.hi == 0) ? 0x0 : 0x3800;
+    bias_b.lo = (fp16_packed_b.lo == 0) ? 0x0 : 0x3800;
+    bias_b.hi = (fp16_packed_b.hi == 0) ? 0x0 : 0x3800;
+
+    fp16_packed_a.lo = (fp16_packed_a.lo == 0x0200) ? 0x0 : fp16_packed_a.lo;
+    fp16_packed_a.hi = (fp16_packed_a.hi == 0x0200) ? 0x0 : fp16_packed_a.hi;
+    fp16_packed_b.lo = (fp16_packed_b.lo == 0x0200) ? 0x0 : fp16_packed_b.lo;
+    fp16_packed_b.hi = (fp16_packed_b.hi == 0x0200) ? 0x0 : fp16_packed_b.hi;
+
+    sign_a.lo = (fp4x4 << 12) & 0x8000;
+    sign_a.hi = (fp4x4 << 8) & 0x8000;
+    sign_b.lo = (fp4x4 << 4) & 0x8000;
+    sign_b.hi = fp4x4 & 0x8000;
+
+    fp16_packed_a = sign_a + bias_a + fp16_packed_a;
+    fp16_packed_b = sign_b + bias_b + fp16_packed_b;
+
+    return as_half4((ushort4)(fp16_packed_a, fp16_packed_b));
+}
+
+static inline float e8m0_to_fp32(uchar x) {
+    int bits;
+    bits = (x == 0) ? 0x00400000 : ((uint) x << 23);
+    return as_float(bits);
+}
+
+#ifdef INTEL_GPU
+#define N_R0_MXFP4 2 // number of rows each subgroup works on
+#define N_SG_MXFP4 2 // number of subgroups in a work group
+#define N_SIMDWIDTH 16 // subgroup size
+#elif defined (ADRENO_GPU)
+#define N_R0_MXFP4 4
+#define N_SG_MXFP4 1
+#define N_SIMDWIDTH 64
+#define SRC0Q_IMG
+#endif
+
+kernel void kernel_mul_mv_id_mxfp4_f32_flat(
+#ifdef SRC0Q_IMG
+    __read_only image1d_buffer_t src0_q,
+#else
+    global uchar * src0_q,
+#endif
+    global uchar * src0_e,
+    global uchar * src1,
+    ulong         offset1,
+    global uchar * src2,
+    ulong         offset2,
+    global uchar * dst,
+    ulong         offsetd,
+    int           ne00,
+    ulong         nb01,
+    ulong         nb02,
+    ulong         nb03,
+    int           ne11,
+    int           ne12,
+    ulong         nb11,
+    ulong         nb12,
+    ulong         nb13,
+    int           ne20,
+    int           ne21,
+    ulong         nb21,
+    int           ne0,
+    int           ne1,
+    int           r2,
+    int           r3
+) {
+    dst  = dst  + offsetd;
+
+    const int iid1 = get_group_id(2) / ne20;
+    const int idx  = get_group_id(2) % ne20;
+
+    uint i02 = ((global uint *) (src2 + offset2 + iid1 * nb21))[idx];
+
+    int i11 = idx % ne11;
+
+    int nb = ne00 / QK_MXFP4;
+
+    uint src0_off = i02*nb02;
+    src0_off /= 17; // 17 = sizeof(block_mxfp4)
+
+    src0_e = src0_e + src0_off;
+
+    dst = dst + (idx * ne0 + iid1 * ne1 * ne0) * sizeof(float);
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+
+    int first_row = (r0 * N_SG_MXFP4 + get_sub_group_id()) * N_R0_MXFP4;
+
+    uint offset_src0 = first_row*nb01;
+    offset_src0 /= 17; // 17 = sizeof(block_mxfp4)
+#ifdef SRC0Q_IMG
+    ulong offset_q = src0_off + offset_src0;
+#else
+    src0_q = src0_q + src0_off*16;
+    global uchar16 * x_q = (global uchar16 *)(src0_q) + offset_src0;
+#endif
+    global uchar * x_e = src0_e + offset_src0;
+
+    const short ix = get_sub_group_local_id() >> 1;
+    const short it = get_sub_group_local_id() & 1;
+
+    float sumf[N_R0_MXFP4] = {0.f};
+
+    src1 = src1 + offset1 + i11 * nb11 + iid1 * nb12;
+    global float * y   = (global float *) (src1 + r1 * nb11);
+    global float * yb = y + ix * QK_MXFP4 + it * 8;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH / 2) {
+        global float4 * y4 = (global float4 *)yb;
+
+        #pragma unroll
+        for (short row = 0; row < N_R0_MXFP4; row++) {
+            uchar xb_e = x_e[row * nb + ib];
+#ifdef SRC0Q_IMG
+            ushort4 xb_q = as_ushort4(read_imageui(src0_q, (offset_q + row * nb + ib) * 2 + it).xy);
+#else
+            ushort4 xb_q = vload4(0, (global ushort *)((global uchar *)(x_q + row * nb + ib) + 8 * it));
+#endif
+
+            half4 fp16x4_0 = mxfp4_to_fp16_packed(xb_q.s0);
+            half4 fp16x4_1 = mxfp4_to_fp16_packed(xb_q.s1);
+            float4 acc1 = y4[0] * (float4)(fp16x4_0.s0, fp16x4_0.s2, fp16x4_1.s0, fp16x4_1.s2);
+            acc1 += y4[4] * (float4)(fp16x4_0.s1, fp16x4_0.s3, fp16x4_1.s1, fp16x4_1.s3);
+
+            fp16x4_0 = mxfp4_to_fp16_packed(xb_q.s2);
+            fp16x4_1 = mxfp4_to_fp16_packed(xb_q.s3);
+            acc1 += y4[1] * (float4)(fp16x4_0.s0, fp16x4_0.s2, fp16x4_1.s0, fp16x4_1.s2);
+            acc1 += y4[5] * (float4)(fp16x4_0.s1, fp16x4_0.s3, fp16x4_1.s1, fp16x4_1.s3);
+
+            sumf[row] += e8m0_to_fp32(xb_e) * ((acc1.s0 + acc1.s1) + (acc1.s2 + acc1.s3));
+        }
+
+        yb += (N_SIMDWIDTH / 2) * QK_MXFP4;
+    }
+
+    global float * dst_f32 = (global float *)dst + (ulong)r1 * ne0;
+
+    for (int row = 0; row < N_R0_MXFP4 && first_row + row < ne0; ++row) {
+        float sum_all = sub_group_reduce_add(sumf[row]);
+        if (get_sub_group_local_id() == 0) {
+            dst_f32[first_row + row] = sum_all;
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/mul_mv_mxfp4_f32_flat.cl

@@ -0,0 +1,167 @@
+#pragma OPENCL EXTENSION cl_khr_fp16 : enable
+
+#ifdef cl_intel_subgroups
+#pragma OPENCL EXTENSION cl_intel_subgroups : enable
+#else
+#pragma OPENCL EXTENSION cl_khr_subgroups : enable
+#endif
+
+#ifdef cl_intel_required_subgroup_size
+#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
+#define INTEL_GPU 1
+#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
+#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
+#elif defined(cl_qcom_reqd_sub_group_size)
+#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
+#define ADRENO_GPU 1
+#define REQD_SUBGROUP_SIZE_64  __attribute__((qcom_reqd_sub_group_size("half")))
+#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
+#endif
+
+#define QK_MXFP4 32
+
+static inline half4 mxfp4_to_fp16_packed(ushort fp4x4) {
+    ushort2 fp16_packed_a, fp16_packed_b, bias_a, bias_b, sign_a, sign_b;
+    fp16_packed_a.lo = (fp4x4 << 9) & 0x0E00;
+    fp16_packed_a.hi = (fp4x4 << 5) & 0x0E00;
+    fp16_packed_b.lo = (fp4x4 << 1) & 0x0E00;
+    fp16_packed_b.hi = (fp4x4 >> 3) & 0x0E00;
+
+    bias_a.lo = (fp16_packed_a.lo == 0) ? 0x0 : 0x3800;
+    bias_a.hi = (fp16_packed_a.hi == 0) ? 0x0 : 0x3800;
+    bias_b.lo = (fp16_packed_b.lo == 0) ? 0x0 : 0x3800;
+    bias_b.hi = (fp16_packed_b.hi == 0) ? 0x0 : 0x3800;
+
+    fp16_packed_a.lo = (fp16_packed_a.lo == 0x0200) ? 0x0 : fp16_packed_a.lo;
+    fp16_packed_a.hi = (fp16_packed_a.hi == 0x0200) ? 0x0 : fp16_packed_a.hi;
+    fp16_packed_b.lo = (fp16_packed_b.lo == 0x0200) ? 0x0 : fp16_packed_b.lo;
+    fp16_packed_b.hi = (fp16_packed_b.hi == 0x0200) ? 0x0 : fp16_packed_b.hi;
+
+    sign_a.lo = (fp4x4 << 12) & 0x8000;
+    sign_a.hi = (fp4x4 << 8) & 0x8000;
+    sign_b.lo = (fp4x4 << 4) & 0x8000;
+    sign_b.hi = fp4x4 & 0x8000;
+
+    fp16_packed_a = sign_a + bias_a + fp16_packed_a;
+    fp16_packed_b = sign_b + bias_b + fp16_packed_b;
+
+    return as_half4((ushort4)(fp16_packed_a, fp16_packed_b));
+}
+
+static inline float e8m0_to_fp32(uchar x) {
+    int bits;
+    bits = (x == 0) ? 0x00400000 : ((uint) x << 23);
+    return as_float(bits);
+}
+
+#ifdef INTEL_GPU
+#define N_R0_MXFP4 2 // number of rows each subgroup works on
+#define N_SG_MXFP4 2 // number of subgroups in a work group
+#define N_SIMDWIDTH 16 // subgroup size
+#elif defined (ADRENO_GPU)
+#define N_R0_MXFP4 2
+#define N_SG_MXFP4 2
+#define N_SIMDWIDTH 64
+#define SRC0Q_IMG
+#endif
+
+#ifdef INTEL_GPU
+REQD_SUBGROUP_SIZE_16
+#elif defined (ADRENO_GPU)
+REQD_SUBGROUP_SIZE_64
+#endif
+kernel void kernel_mul_mv_mxfp4_f32_flat(
+#ifdef SRC0Q_IMG
+    __read_only image1d_buffer_t src0_q,
+#else
+    global uchar * src0_q,
+#endif
+    global uchar * src0_e,
+    global uchar * src1,
+    ulong          offset1,
+    global uchar * dst,
+    ulong          offsetd,
+    int ne00,
+    ulong nb01,
+    ulong nb02,
+    ulong nb03,
+    int ne12,
+    ulong nb11,
+    ulong nb12,
+    ulong nb13,
+    int ne0,
+    int ne1,
+    int r2,
+    int r3
+) {
+    src1 = src1 + offset1;
+    dst = dst + offsetd;
+
+    int nb = ne00 / QK_MXFP4;
+
+    int r0 = get_group_id(0);
+    int r1 = get_group_id(1);
+    int im = get_group_id(2);
+
+    int first_row = (r0 * N_SG_MXFP4 + get_sub_group_id()) * N_R0_MXFP4;
+
+    uint i12 = im % ne12;
+    uint i13 = im / ne12;
+
+    uint offset_src0 = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
+    // 17 = sizeof(block_mxfp4)
+    offset_src0 /= 17;
+#ifdef SRC0Q_IMG
+    ulong offset_q = offset_src0;
+#else
+    global uchar16 * x_q = (global uchar16 *)(src0_q) + offset_src0;
+#endif
+    global uchar * x_e = src0_e + offset_src0;
+
+    ulong offset_src1 = r1 * nb11 + i12 * nb12 + i13 * nb13;
+    global float * y = (global float *)(src1 + offset_src1);
+
+    const short ix = get_sub_group_local_id() >> 1;  // 0...15
+    const short it = get_sub_group_local_id() & 1;  // 0 or 1
+
+    float sumf[N_R0_MXFP4] = {0.f};
+
+    global float * yb = y + ix * QK_MXFP4 + it * 8;
+
+    for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/2) {
+        global float4 * y4 = (global float4 *)yb;
+
+        #pragma unroll
+        for (short row = 0; row < N_R0_MXFP4; row++) {
+            uchar xb_e = x_e[row * nb + ib];
+#ifdef SRC0Q_IMG
+            ushort4 xb_q = as_ushort4(read_imageui(src0_q, (offset_q + row * nb + ib) * 2 + it).xy);
+#else
+            ushort4 xb_q = vload4(0, (global ushort *)((global uchar *)(x_q + row * nb + ib) + 8 * it));
+#endif
+
+            half4 fp16x4_0 = mxfp4_to_fp16_packed(xb_q.s0);
+            half4 fp16x4_1 = mxfp4_to_fp16_packed(xb_q.s1);
+            float4 acc1 = y4[0] * (float4)(fp16x4_0.s0, fp16x4_0.s2, fp16x4_1.s0, fp16x4_1.s2);
+            acc1 += y4[4] * (float4)(fp16x4_0.s1, fp16x4_0.s3, fp16x4_1.s1, fp16x4_1.s3);
+
+            fp16x4_0 = mxfp4_to_fp16_packed(xb_q.s2);
+            fp16x4_1 = mxfp4_to_fp16_packed(xb_q.s3);
+            acc1 += y4[1] * (float4)(fp16x4_0.s0, fp16x4_0.s2, fp16x4_1.s0, fp16x4_1.s2);
+            acc1 += y4[5] * (float4)(fp16x4_0.s1, fp16x4_0.s3, fp16x4_1.s1, fp16x4_1.s3);
+
+            sumf[row] += e8m0_to_fp32(xb_e) * ((acc1.s0 + acc1.s1) + (acc1.s2 + acc1.s3));
+        }
+
+        yb += (N_SIMDWIDTH/2) * QK_MXFP4;
+    }
+
+    global float * dst_f32 = (global float *) dst + (ulong)im*ne0*ne1 + (ulong)r1*ne0;
+
+    for (int row = 0; row < N_R0_MXFP4 && first_row + row < ne0; ++row) {
+        float sum_all = sub_group_reduce_add(sumf[row]);
+        if (get_sub_group_local_id() == 0) {
+            dst_f32[first_row + row] = sum_all;
+        }
+    }
+}

ggml/src/ggml-opencl/kernels/tsembd.cl

@@ -26,8 +26,8 @@ kernel void kernel_timestep_embedding(
     local_half_dim = logical_dim / 2;
     local_embed_data_ptr = (global float *)((global char *)local_dst_output_base_ptr + local_i * dst_nb1_bytes);
 
-    if (logical_dim % 2 != 0 && local_j == ((logical_dim + 1) / 2)) {
-        local_embed_data_ptr[logical_dim] = 0.0f;
+    if (logical_dim % 2 != 0 && local_j == local_half_dim) {
+        local_embed_data_ptr[2 * local_half_dim] = 0.0f;
     }
 
     if (local_j >= local_half_dim) {

ggml/src/ggml-rpc/ggml-rpc.cpp

@@ -795,7 +795,7 @@ static ggml_backend_i ggml_backend_rpc_interface = {
     /* .graph_compute           = */ ggml_backend_rpc_graph_compute,
     /* .event_record            = */ NULL,
     /* .event_wait              = */ NULL,
-    /* .optimize_graph          = */ NULL,
+    /* .graph_optimize          = */ NULL,
 };
 
 ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) {

ggml/src/ggml-sycl/binbcast.cpp

@@ -225,9 +225,9 @@ struct bin_bcast_sycl {
                     dpct::has_capability_or_fail(stream->get_device(),
                                                  {sycl::aspect::fp16});
 
-                    sycl_parallel_for(
-                        stream,
-                        sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) * sycl::range<3>(1, 1, block_size),
+                    stream->parallel_for(
+                        sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) *
+                                              sycl::range<3>(1, 1, block_size),
                                           sycl::range<3>(1, 1, block_size)),
                         [=](sycl::nd_item<3> item_ct1) {
                             k_bin_bcast_unravel<bin_op>(
@@ -246,8 +246,9 @@ struct bin_bcast_sycl {
                 dpct::has_capability_or_fail(stream->get_device(),
                                              {sycl::aspect::fp16});
 
-                sycl_parallel_for(
-                    stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                stream->parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         k_bin_bcast<bin_op>(src0_dd, src1_dd, dst_dd, ne0, ne1,
                                             ne2, ne3, ne10, ne11, ne12, ne13,
                                             s1, s2, s3, s01, s02, s03, s11, s12, s13,
@@ -302,6 +303,10 @@ inline void ggml_sycl_op_sub(ggml_backend_sycl_context & ctx, ggml_tensor *dst)
     ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_sub>>(ctx, dst->src[0], dst->src[1], dst);
 }
 
+inline void ggml_sycl_op_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_count_equal>>(ctx, dst->src[0], dst->src[1], dst);
+}
+
 inline void ggml_sycl_op_mul(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {
 
     ggml_sycl_op_bin_bcast<bin_bcast_sycl<op_mul>>(ctx, dst->src[0], dst->src[1], dst);
@@ -327,6 +332,11 @@ void ggml_sycl_sub(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     ggml_sycl_op_sub(ctx, dst);
 }
 
+void ggml_sycl_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    ggml_sycl_op_count_equal(ctx, dst);
+}
+
 void ggml_sycl_mul(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
     ggml_sycl_op_mul(ctx, dst);

ggml/src/ggml-sycl/binbcast.hpp

@@ -16,6 +16,12 @@ static __dpct_inline__ float op_sub(const float a, const float b) {
     return a - b;
 }
 
+static __dpct_inline__ float op_count_equal(const float a, const float b) {
+    return (a == b) ? 1.0f : 0.0f;
+}
+
+void ggml_sycl_count_equal(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
 static __dpct_inline__ float op_mul(const float a, const float b) {
     return a * b;
 }

ggml/src/ggml-sycl/concat.cpp

@@ -89,24 +89,33 @@ static void concat_f32_sycl(const float *x, const float *y, float *dst,
   sycl::range<3> gridDim(ne2, ne1, num_blocks);
   switch (dim) {
   case 0:
-      sycl_parallel_for(stream,
-                        sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
-                                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
-                        [=](sycl::nd_item<3> item_ct1) { concat_f32_dim0(x, y, dst, ne0, ne00, item_ct1); });
-      break;
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+          concat_f32_dim0(x, y, dst, ne0, ne00, item_ct1);
+        });
+    break;
   case 1:
-      sycl_parallel_for(stream,
-                        sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
-                                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
-                        [=](sycl::nd_item<3> item_ct1) { concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1); });
-      break;
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+          concat_f32_dim1(x, y, dst, ne0, ne01, item_ct1);
+        });
+    break;
   // dim >=2 will be dispatched to the default path
   default:
-      sycl_parallel_for(stream,
-                        sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
-                                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
-                        [=](sycl::nd_item<3> item_ct1) { concat_f32_dim2(x, y, dst, ne0, ne02, item_ct1); });
-      break;
+    stream->parallel_for(
+        sycl::nd_range<3>(gridDim *
+                              sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE),
+                          sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) {
+          concat_f32_dim2(x, y, dst, ne0, ne02, item_ct1);
+        });
+    break;
   }
 }
 
@@ -120,7 +129,7 @@ static void concat_f32_sycl_non_cont(
     int64_t ne2, int64_t ne3, uint64_t nb0, uint64_t nb1, uint64_t nb2,
     uint64_t nb3, int32_t dim) {
   sycl::range<3> gridDim(ne3, ne2, ne1);
-  sycl_parallel_for(stream, sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+  stream->parallel_for(sycl::nd_range<3>(gridDim, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
       int64_t i3 = item_ct1.get_group(0);
       int64_t i2 = item_ct1.get_group(1);
       int64_t i1 = item_ct1.get_group(2);

ggml/src/ggml-sycl/conv.cpp

@@ -59,10 +59,16 @@ static void conv_transpose_1d_f32_f32_sycl(
     const int num_blocks = (output_size + SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE - 1) / SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE;
     const sycl::range<3> block_dims(1, 1, SYCL_CONV_TRANPOSE_1D_BLOCK_SIZE);
     const sycl::range<3> block_nums(1, 1, num_blocks);
-    sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
-        conv_transpose_1d_kernel(s0, output_size, src0_ne0, src0_ne1, src0_ne2, src1_ne0, dst_ne0, src0, src1, dst,
-                                 item_ct1);
-    });
+    stream->parallel_for(
+        sycl::nd_range<3>(
+            block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) {
+            conv_transpose_1d_kernel(
+                s0, output_size,
+                src0_ne0, src0_ne1, src0_ne2,
+                src1_ne0, dst_ne0,
+                src0, src1, dst, item_ct1);
+        });
 }
 
 void ggml_sycl_op_conv_transpose_1d(ggml_backend_sycl_context & ctx, ggml_tensor *dst) {

ggml/src/ggml-sycl/convert.cpp

@@ -33,11 +33,14 @@ static void dequantize_block_sycl(const void *__restrict__ vx,
     {
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
-        sycl_parallel_for(
-            stream,
-            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block<qk, qr, dequantize_kernel>(vx, y, k, item_ct1); });
+        stream->parallel_for(
+            sycl::nd_range<3>(
+                sycl::range<3>(1, 1, num_blocks) *
+                    sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE),
+                sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)),
+            [=](sycl::nd_item<3> item_ct1) {
+                dequantize_block<qk, qr, dequantize_kernel>(vx, y, k, item_ct1);
+            });
     }
 }
 
@@ -50,18 +53,24 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q2_K(vx, y, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q2_K(vx, y, item_ct1);
+                             });
     }
 #else
     {
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q2_K(vx, y, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q2_K(vx, y, item_ct1);
+                             });
     }
 
 #endif
@@ -76,18 +85,24 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q3_K(vx, y, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q3_K(vx, y, item_ct1);
+                             });
     }
 #else
     {
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q3_K(vx, y, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q3_K(vx, y, item_ct1);
+                             });
     }
 #endif
 }
@@ -101,9 +116,12 @@ static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q4_0(vx, y, nb32, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_0(vx, y, nb32, item_ct1);
+                             });
     }
 }
 
@@ -117,12 +135,13 @@ static void dequantize_row_q4_0_sycl_reorder(const void *vx, dst_t *y, const int
     int constexpr WARP_K = WARP_SIZE * QK4_0;
     const int n_warp = (k + WARP_K - 1) / WARP_K;
     GGML_ASSERT(k % 2 == 0);
-    sycl_parallel_for(stream,
-                      sycl::nd_range<3>(sycl::range<3>(1, 1, n_warp) * sycl::range<3>(1, 1, WARP_SIZE),
-                                        sycl::range<3>(1, 1, WARP_SIZE)),
-                      [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                          dequantize_block_q4_0_reorder(vx, y, k, item_ct1);
-                      });
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, n_warp) *
+        sycl::range<3>(1, 1, WARP_SIZE),
+        sycl::range<3>(1, 1, WARP_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
+            dequantize_block_q4_0_reorder(vx, y, k, item_ct1);
+        });
+
 }
 
 template <typename dst_t>
@@ -134,9 +153,12 @@ static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q4_1(vx, y, nb32, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_1(vx, y, nb32, item_ct1);
+                             });
     }
 }
 
@@ -149,13 +171,14 @@ static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler &cgh) {
             sycl::local_accessor<uint8_t, 1> scale_local_acc(sycl::range<1>(12), cgh);
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                [=](sycl::nd_item<3> item_ct1) {
-                    dequantize_block_q4_K(vx, y, get_pointer(scale_local_acc), item_ct1);
-                });
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q4_K(vx, y, get_pointer(scale_local_acc), item_ct1);
+                             });
         });
     }
 }
@@ -168,13 +191,13 @@ static void dequantize_row_q4_K_sycl_reorder(const void * vx, dst_t * y, const i
 
     dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
 
-    sycl_launch(stream, [&](sycl::handler & cgh) {
+    stream->submit([&](sycl::handler & cgh) {
         sycl::local_accessor<uint8_t, 1> scale_local_acc(sycl::range<1>(12), cgh);
 
-        sycl_parallel_for<1>(cgh, sycl::nd_range<1>(sycl::range<1>(global_size), sycl::range<1>(local_size)),
-                             [=](sycl::nd_item<1> item_ct1) {
-                                 dequantize_block_q4_K_reorder(vx, y, get_pointer(scale_local_acc), item_ct1, nb);
-                             });
+        cgh.parallel_for(sycl::nd_range<1>(sycl::range<1>(global_size), sycl::range<1>(local_size)),
+                         [=](sycl::nd_item<1> item_ct1) {
+                             dequantize_block_q4_K_reorder(vx, y, get_pointer(scale_local_acc), item_ct1, nb);
+                         });
     });
 }
 
@@ -187,18 +210,24 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q5_K(vx, y, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q5_K(vx, y, item_ct1);
+                             });
     }
 #else
     {
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q5_K(vx, y, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q5_K(vx, y, item_ct1);
+                             });
     }
 
 #endif
@@ -213,18 +242,24 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K(vx, y, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 64),
+                                               sycl::range<3>(1, 1, 64)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q6_K(vx, y, item_ct1);
+                             });
     }
 #else
     {
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-            [=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K(vx, y, item_ct1); });
+        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_q6_K(vx, y, item_ct1);
+                             });
     }
 
 #endif
@@ -236,9 +271,9 @@ static void dequantize_row_q6_K_sycl_reorder(const void * vx, dst_t * y, const i
 
     dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
 
-    sycl_parallel_for(stream,
-                      sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
-                      [=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K_reorder(vx, y, item_ct1, nb); });
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 64), sycl::range<3>(1, 1, 64)),
+        [=](sycl::nd_item<3> item_ct1) { dequantize_block_q6_K_reorder(vx, y, item_ct1, nb); });
 }
 
 template <typename dst_t>
@@ -249,10 +284,15 @@ static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq1_s(vx, y, item_ct1, iq1s_grid_gpu); });
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq1_s(
+                                     vx, y, item_ct1, iq1s_grid_gpu
+                                     );
+                             });
         });
     }
 }
@@ -265,10 +305,15 @@ static void dequantize_row_iq1_m_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq1_m(vx, y, item_ct1, iq1s_grid_gpu); });
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq1_m(
+                                     vx, y, item_ct1, iq1s_grid_gpu
+                                     );
+                             });
         });
     }
 }
@@ -281,12 +326,15 @@ static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int64_t
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                [=](sycl::nd_item<3> item_ct1) {
-                    dequantize_block_iq2_xxs(vx, y, item_ct1, iq2xxs_grid, ksigns_iq2xs, kmask_iq2xs);
-                });
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_xxs(
+                                     vx, y, item_ct1, iq2xxs_grid,
+                                     ksigns_iq2xs, kmask_iq2xs);
+                             });
         });
     }
 }
@@ -299,12 +347,15 @@ static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int64_t k
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                [=](sycl::nd_item<3> item_ct1) {
-                    dequantize_block_iq2_xs(vx, y, item_ct1, iq2xs_grid, ksigns_iq2xs, kmask_iq2xs);
-                });
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_xs(
+                                     vx, y, item_ct1, iq2xs_grid,
+                                     ksigns_iq2xs, kmask_iq2xs);
+                             });
         });
     }
 }
@@ -317,10 +368,13 @@ static void dequantize_row_iq2_s_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq2_s(vx, y, item_ct1); });
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq2_s(vx, y, item_ct1);
+                             });
         });
     }
 }
@@ -334,12 +388,15 @@ static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int64_t
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                [=](sycl::nd_item<3> item_ct1) {
-                    dequantize_block_iq3_xxs(vx, y, item_ct1, iq3xxs_grid, ksigns_iq2xs, kmask_iq2xs);
-                });
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq3_xxs(
+                                     vx, y, item_ct1, iq3xxs_grid,
+                                     ksigns_iq2xs, kmask_iq2xs);
+                             });
         });
     }
 }
@@ -352,10 +409,14 @@ static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int64_t k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq3_s(vx, y, item_ct1, kmask_iq2xs, iq3s_grid); });
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                                   sycl::range<3>(1, 1, 32),
+                                               sycl::range<3>(1, 1, 32)),
+                             [=](sycl::nd_item<3> item_ct1) {
+                                 dequantize_block_iq3_s(
+                                     vx, y, item_ct1, kmask_iq2xs, iq3s_grid);
+                             });
         });
     }
 }
@@ -371,11 +432,14 @@ static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int64_t k
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
-                sycl_parallel_for(
-                    cgh,
-                    sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                    [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq4_xs(vx, y, item_ct1); });
+            stream->submit([&](sycl::handler &cgh) {
+                  cgh.parallel_for(
+                      sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                            sycl::range<3>(1, 1, 32),
+                                        sycl::range<3>(1, 1, 32)),
+                      [=](sycl::nd_item<3> item_ct1) {
+                            dequantize_block_iq4_xs(vx, y, item_ct1);
+                      });
             });
       }
 #endif
@@ -389,11 +453,14 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int64_t k
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
-                sycl_parallel_for(
-                    cgh,
-                    sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
-                    [=](sycl::nd_item<3> item_ct1) { dequantize_block_iq4_nl(vx, y, item_ct1); });
+            stream->submit([&](sycl::handler &cgh) {
+                  cgh.parallel_for(
+                      sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+                                            sycl::range<3>(1, 1, 32),
+                                        sycl::range<3>(1, 1, 32)),
+                      [=](sycl::nd_item<3> item_ct1) {
+                            dequantize_block_iq4_nl(vx, y, item_ct1);
+                      });
             });
       }
 }

ggml/src/ggml-sycl/cpy.cpp

@@ -201,8 +201,7 @@ static void ggml_cpy_f16_f32_sycl(const char * cx, char * cdst, const int ne, co
     {
         dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
 
-        sycl_parallel_for(
-            stream,
+        stream->parallel_for(
             sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
@@ -220,8 +219,7 @@ static void ggml_cpy_f32_f32_sycl(const char * cx, char * cdst, const int ne, co
     {
         dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
 
-        sycl_parallel_for(
-            stream,
+        stream->parallel_for(
             sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
@@ -239,8 +237,7 @@ static void ggml_cpy_f32_f16_sycl(const char * cx, char * cdst, const int ne, co
     {
         dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
 
-        sycl_parallel_for(
-            stream,
+        stream->parallel_for(
             sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
@@ -256,11 +253,11 @@ static void ggml_cpy_f32_q8_0_sycl(const char * cx, char * cdst, const int ne, c
                                    const int nb12, const int nb13, queue_ptr stream) {
     GGML_ASSERT(ne % QK8_0 == 0);
     const int num_blocks = ne / QK8_0;
-    sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
-                                                              ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
+                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+                         });
 }
 
 static void ggml_cpy_q8_0_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
@@ -268,11 +265,11 @@ static void ggml_cpy_q8_0_f32_sycl(const char * cx, char * cdst, const int ne, c
                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
                                    const int nb12, const int nb13, queue_ptr stream) {
     const int num_blocks = ne;
-    sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_q_f32<cpy_blck_q8_0_f32, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
-                                                              ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_q_f32<cpy_blck_q8_0_f32, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
+                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+                         });
 }
 
 static void ggml_cpy_f32_q4_0_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
@@ -281,11 +278,11 @@ static void ggml_cpy_f32_q4_0_sycl(const char * cx, char * cdst, const int ne, c
                                    const int nb12, const int nb13, queue_ptr stream) {
     GGML_ASSERT(ne % QK4_0 == 0);
     const int num_blocks = ne / QK4_0;
-    sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
-                                                              ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
+                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+                         });
 }
 
 static void ggml_cpy_q4_0_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
@@ -293,9 +290,8 @@ static void ggml_cpy_q4_0_f32_sycl(const char * cx, char * cdst, const int ne, c
                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
                                    const int nb12, const int nb13, queue_ptr stream) {
     const int num_blocks = ne;
-    sycl_parallel_for(
-        stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-        [=](sycl::nd_item<3> item_ct1) {
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
             cpy_q_f32<cpy_blck_q_f32<dequantize_q4_0, QK4_0>, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
                                                                      nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                                                      item_ct1);
@@ -308,11 +304,11 @@ static void ggml_cpy_f32_q4_1_sycl(const char * cx, char * cdst, const int ne, c
                                    const int nb12, const int nb13, queue_ptr stream) {
     GGML_ASSERT(ne % QK4_1 == 0);
     const int num_blocks = ne / QK4_1;
-    sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
-                                                              ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
+                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+                         });
 }
 
 static void ggml_cpy_q4_1_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
@@ -320,9 +316,8 @@ static void ggml_cpy_q4_1_f32_sycl(const char * cx, char * cdst, const int ne, c
                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
                                    const int nb12, const int nb13, queue_ptr stream) {
     const int num_blocks = ne;
-    sycl_parallel_for(
-        stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-        [=](sycl::nd_item<3> item_ct1) {
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
             cpy_q_f32<cpy_blck_q_f32<dequantize_q4_1, QK4_1>, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
                                                                      nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                                                      item_ct1);
@@ -335,11 +330,11 @@ static void ggml_cpy_f32_q5_0_sycl(const char * cx, char * cdst, const int ne, c
                                    const int nb12, const int nb13, queue_ptr stream) {
     GGML_ASSERT(ne % QK5_0 == 0);
     const int num_blocks = ne / QK5_0;
-    sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
-                                                              ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
+                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+                         });
 }
 
 static void ggml_cpy_q5_0_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
@@ -347,9 +342,8 @@ static void ggml_cpy_q5_0_f32_sycl(const char * cx, char * cdst, const int ne, c
                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
                                    const int nb12, const int nb13, queue_ptr stream) {
     const int num_blocks = ne;
-    sycl_parallel_for(
-        stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-        [=](sycl::nd_item<3> item_ct1) {
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
             cpy_q_f32<cpy_blck_q_f32<dequantize_q5_0, QK5_0>, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
                                                                      nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                                                      item_ct1);
@@ -362,11 +356,11 @@ static void ggml_cpy_f32_q5_1_sycl(const char * cx, char * cdst, const int ne, c
                                    const int nb12, const int nb13, queue_ptr stream) {
     GGML_ASSERT(ne % QK5_1 == 0);
     const int num_blocks = ne / QK5_1;
-    sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
-                                                              ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
+                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+                         });
 }
 
 static void ggml_cpy_q5_1_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
@@ -374,9 +368,8 @@ static void ggml_cpy_q5_1_f32_sycl(const char * cx, char * cdst, const int ne, c
                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
                                    const int nb12, const int nb13, queue_ptr stream) {
     const int num_blocks = ne;
-    sycl_parallel_for(
-        stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-        [=](sycl::nd_item<3> item_ct1) {
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
             cpy_q_f32<cpy_blck_q_f32<dequantize_q5_1, QK5_1>, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
                                                                      nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                                                      item_ct1);
@@ -389,11 +382,11 @@ static void ggml_cpy_f32_iq4_nl_sycl(const char * cx, char * cdst, const int ne,
                                      const int nb12, const int nb13, queue_ptr stream) {
     GGML_ASSERT(ne % QK4_NL == 0);
     const int num_blocks = ne / QK4_NL;
-    sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
-                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
+                                                   ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
 }
 
 static void ggml_cpy_f16_f16_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
@@ -404,8 +397,7 @@ static void ggml_cpy_f16_f16_sycl(const char * cx, char * cdst, const int ne, co
     {
         dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
 
-        sycl_parallel_for(
-            stream,
+        stream->parallel_for(
             sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
@@ -424,8 +416,7 @@ static void ggml_cpy_i16_i16_sycl(const char * cx, char * cdst, const int ne, co
         // dpct::has_capability_or_fail(stream->get_device(),
         //                              {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream,
+        stream->parallel_for(
             sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
@@ -444,8 +435,7 @@ static void ggml_cpy_i32_i32_sycl(const char * cx, char * cdst, const int ne, co
         // dpct::has_capability_or_fail(stream->get_device(),
         //                              {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream,
+        stream->parallel_for(
             sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                               sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
             [=](sycl::nd_item<3> item_ct1) {
@@ -460,13 +450,11 @@ static void ggml_cpy_q8_0_q8_0(const char * cx, char * cdst, const int ne, const
                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
                                    const int nb12, const int nb13, queue_ptr stream) {
     const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
-    sycl_parallel_for(stream,
-                      sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                                        sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_q_q<block_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
-                                                     ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
 }
 
 
@@ -475,13 +463,11 @@ static void ggml_cpy_q5_0_q5_0(const char * cx, char * cdst, const int ne, const
                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
                                    const int nb12, const int nb13, queue_ptr stream) {
     const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
-    sycl_parallel_for(stream,
-                      sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                                        sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_q_q<block_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
-                                                     ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
 }
 
 
@@ -491,13 +477,11 @@ static void ggml_cpy_q5_1_q5_1(const char * cx, char * cdst, const int ne, const
                                    const int nb12, const int nb13, queue_ptr stream) {
     const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
 
-    sycl_parallel_for(stream,
-                      sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                                        sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_q_q<block_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
-                                                     ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
 }
 
 
@@ -506,13 +490,10 @@ static void ggml_cpy_q4_0_q4_0(const char * cx, char * cdst, const int ne, const
                                    const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
                                    const int nb12, const int nb13, queue_ptr stream) {
     const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
-    sycl_parallel_for(stream,
-                      sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                                        sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-                      [=](sycl::nd_item<3> item_ct1) {
-                          cpy_q_q<block_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
-                                                     ne12, nb10, nb11, nb12, nb13, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
 }
 
 
@@ -522,13 +503,10 @@ static void ggml_cpy_q4_1_q4_1(const char * cx, char * cdst, const int ne, const
                                    const int nb12, const int nb13, queue_ptr stream) {
 
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
-   sycl_parallel_for(stream,
-                     sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                                       sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-                     [=](sycl::nd_item<3> item_ct1) {
-                         cpy_q_q<block_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
-                                                    ne12, nb10, nb11, nb12, nb13, item_ct1);
-                     });
+   stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+            cpy_q_q<block_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
 }
 
 void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1) try {

ggml/src/ggml-sycl/dmmv.cpp

@@ -208,10 +208,12 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols, nrows, item_ct1);
-                          });
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols,
+                                                          nrows, item_ct1);
+            });
     }
 }
 
@@ -875,11 +877,12 @@ static void dequantize_mul_mat_vec_q4_0_sycl_reorder(const void *vx, const dfloa
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              dequantize_mul_mat_vec_reorder<QK4_0, QR4_0, dequantize_q4_0_reorder>(vx, y, dst, ncols,
-                                                                                                    nrows, item_ct1);
-                          });
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec_reorder<QK4_0, QR4_0, dequantize_q4_0_reorder>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
     }
 }
 
@@ -897,10 +900,12 @@ static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(vx, y, dst, ncols, nrows, item_ct1);
-                          });
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
     }
 }
 
@@ -916,10 +921,12 @@ static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(vx, y, dst, ncols, nrows, item_ct1);
-                          });
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
     }
 }
 
@@ -935,10 +942,12 @@ static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(vx, y, dst, ncols, nrows, item_ct1);
-                          });
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
     }
 }
 
@@ -954,10 +963,12 @@ static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(vx, y, dst, ncols, nrows, item_ct1);
-                          });
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
     }
 }
 
@@ -973,10 +984,12 @@ static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(vx, y, dst, ncols, nrows, item_ct1);
-                          });
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(
+                    vx, y, dst, ncols, nrows, item_ct1);
+            });
     }
 }
 
@@ -989,10 +1002,11 @@ static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y,
     const int block_num_y = (nrows + ny - 1) / ny;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
-    sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                      [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
-                          dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
 }
 
 static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y,
@@ -1004,10 +1018,11 @@ static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y,
     const int block_num_y = (nrows + ny - 1) / ny;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
-    sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                      [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
-                          dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
 }
 
 static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y,
@@ -1019,10 +1034,11 @@ static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y,
     const int block_num_y = (nrows + ny - 1) / ny;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
-    sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                      [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
-                          dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
 }
 
 static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y,
@@ -1031,10 +1047,11 @@ static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y,
                                              dpct::queue_ptr stream) {
     GGML_ASSERT(ncols % QK_K == 0);
     const sycl::range<3> block_dims(1, 1, QK_WARP_SIZE);
-    sycl_parallel_for(stream, sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
-                      [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
-                          dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1);
+        });
 }
 
 static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
@@ -1046,10 +1063,11 @@ static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
     const int block_num_y = (nrows + ny - 1) / ny;
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, ny, QK_WARP_SIZE);
-    sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                      [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
-                          dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1);
-                      });
+    stream->parallel_for(
+        sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(QK_WARP_SIZE)]] {
+            dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1);
+        });
 }
 
 void ggml_sycl_op_dequantize_mul_mat_vec(

ggml/src/ggml-sycl/dpct/helper.hpp

@@ -13,10 +13,10 @@
 #ifndef GGML_SYCL_DPCT_HELPER_HPP
 #define GGML_SYCL_DPCT_HELPER_HPP
 
-#include <map>
 #include <sycl/sycl.hpp>
 #include <sycl/half_type.hpp>
 #include <syclcompat/math.hpp>
+#include <map>
 
 #ifdef GGML_SYCL_USE_INTEL_ONEMKL
 #include <oneapi/mkl.hpp>
@@ -118,36 +118,6 @@ inline auto get_onemath_backend(sycl::queue& queue)
 #endif
 }
 
-#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS
-    namespace syclex = sycl::ext::oneapi::experimental;
-#endif
-
-template <int NR, typename Func>
-__dpct_inline__ void sycl_parallel_for(sycl::handler & cgh, sycl::nd_range<NR> nd_range, Func && func) {
-#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS
-    syclex::nd_launch(cgh, nd_range, func);
-#else
-    cgh.parallel_for(nd_range, func);
-#endif
-}
-
-template <int NR, typename Func>
-__dpct_inline__ void sycl_parallel_for(sycl::queue * q, sycl::nd_range<NR> nd_range, Func && func) {
-#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS
-    syclex::nd_launch(*q, nd_range, func);
-#else
-    q->parallel_for(nd_range, func);
-#endif
-}
-
-template <typename Func> __dpct_inline__ void sycl_launch(sycl::queue * stream, Func && func) {
-#ifdef SYCL_EXT_ONEAPI_ENQUEUE_FUNCTIONS
-    syclex::submit(*stream, func);
-#else
-    stream->submit(func);
-#endif
-}
-
 namespace dpct
 {
     typedef sycl::queue *queue_ptr;

ggml/src/ggml-sycl/element_wise.cpp

@@ -407,7 +407,7 @@ static void acc_f32_sycl(const float *x, const float *y, float *dst,
                          const int ne12, const int nb1, const int nb2,
                          const int offset, queue_ptr stream) {
     int num_blocks = ceil_div(n_elements, SYCL_ACC_BLOCK_SIZE);
-    sycl_parallel_for(stream,
+    stream->parallel_for(
         sycl::nd_range<1>(sycl::range<1>(num_blocks) *
                               sycl::range<1>(SYCL_ACC_BLOCK_SIZE),
                           sycl::range<1>(SYCL_ACC_BLOCK_SIZE)),
@@ -425,8 +425,8 @@ static void upscale_sycl(const T *x, T *dst, const int nb00, const int nb01,
     int dst_size = ne10 * ne11 * ne12 * ne13;
     int num_blocks = ceil_div(dst_size, SYCL_UPSCALE_BLOCK_SIZE);
     sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE);
-    sycl_parallel_for<1>(
-        stream, sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
+    stream->parallel_for(
+        sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
             upscale(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1);
         });
 }
@@ -437,7 +437,7 @@ static void pad_sycl(const T *x, T *dst, const int ne00,
                          const int ne1, const int ne2, queue_ptr stream) {
     int num_blocks = ceil_div(ne0, SYCL_PAD_BLOCK_SIZE);
     sycl::range<3> gridDim(ne2, ne1, num_blocks);
-    sycl_parallel_for(stream,
+    stream->parallel_for(
                       sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE),
                                         sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)),
                       [=](sycl::nd_item<3> item_ct1) { pad(x, dst, ne0, ne00, ne01, ne02, item_ct1); });
@@ -639,7 +639,7 @@ static inline void ggml_sycl_op_sgn(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, 256);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
                                   sycl::range<1>(256)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -652,7 +652,7 @@ static inline void ggml_sycl_op_abs(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, 256);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
                                   sycl::range<1>(256)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -665,7 +665,7 @@ static inline void ggml_sycl_op_elu(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, 256);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(256),
                                   sycl::range<1>(256)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -678,7 +678,7 @@ static inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, ggml_tenso
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_SILU_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SILU_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SILU_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -691,7 +691,7 @@ static inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, ggml_tenso
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_GELU_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_GELU_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -704,7 +704,7 @@ static inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, ggml
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_GELU_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_GELU_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -717,7 +717,7 @@ static inline void ggml_sycl_op_gelu_erf(ggml_backend_sycl_context & ctx, ggml_t
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_GELU_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_GELU_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_GELU_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -730,7 +730,7 @@ static inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, ggml_tenso
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_TANH_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_TANH_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_TANH_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -743,7 +743,7 @@ static inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, ggml_tenso
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_RELU_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_RELU_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_RELU_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -756,7 +756,7 @@ static inline void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, ggm
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_HARDSIGMOID_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_HARDSIGMOID_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_HARDSIGMOID_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -769,7 +769,7 @@ static inline void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, ggml_
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_HARDSWISH_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_HARDSWISH_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_HARDSWISH_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -782,7 +782,7 @@ static inline void ggml_sycl_op_exp(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_EXP_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_EXP_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_EXP_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -795,7 +795,7 @@ static inline void ggml_sycl_op_log(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_EXP_BLOCK_SIZE); // Using EXP block size
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_EXP_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_EXP_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -808,7 +808,7 @@ static inline void ggml_sycl_op_neg(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_NEG_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_NEG_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_NEG_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -821,7 +821,7 @@ static inline void ggml_sycl_op_step(ggml_backend_sycl_context & ctx, ggml_tenso
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_NEG_BLOCK_SIZE); // Using NEG block size
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_NEG_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_NEG_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -834,7 +834,7 @@ static inline void ggml_sycl_op_sigmoid(ggml_backend_sycl_context & ctx, ggml_te
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_SIGMOID_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SIGMOID_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SIGMOID_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -847,7 +847,7 @@ static inline void ggml_sycl_op_sqrt(ggml_backend_sycl_context & ctx, ggml_tenso
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_SQRT_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SQRT_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SQRT_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -860,7 +860,7 @@ static inline void ggml_sycl_op_sin(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_SIN_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SIN_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SIN_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -873,7 +873,7 @@ static inline void ggml_sycl_op_cos(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_SIN_BLOCK_SIZE); // Using SIN block size
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SIN_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SIN_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -888,7 +888,7 @@ static inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, ggml
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream, float slope) {
             const int num_blocks = ceil_div(k_elements, SYCL_RELU_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_RELU_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_RELU_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -901,7 +901,7 @@ static inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, ggml_tensor
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream) {
             const int num_blocks = ceil_div(k_elements, SYCL_SQR_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_SQR_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_SQR_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -935,7 +935,7 @@ static inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, ggml_tens
     ggml_sycl_detail::dispatch_ggml_sycl_op_unary(ctx, dst,
         [](const auto* src, auto* dst_ptr, int k_elements, queue_ptr stream, float min_arg, float max_arg) {
             const int num_blocks = ceil_div(k_elements, SYCL_CLAMP_BLOCK_SIZE);
-            sycl_parallel_for(stream,
+            stream->parallel_for(
                 sycl::nd_range<1>(sycl::range<1>(num_blocks) * sycl::range<1>(SYCL_CLAMP_BLOCK_SIZE),
                                   sycl::range<1>(SYCL_CLAMP_BLOCK_SIZE)),
                 [=](sycl::nd_item<1> item_ct1) {
@@ -967,7 +967,7 @@ static inline void ggml_sycl_op_geglu(ggml_backend_sycl_context & ctx, ggml_tens
     ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
-            sycl_parallel_for(main_stream,
+            main_stream->parallel_for(
                     sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
                 gated_op_fused_geglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });
@@ -978,7 +978,7 @@ static inline void ggml_sycl_op_reglu(ggml_backend_sycl_context & ctx, ggml_tens
     ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_RELU_BLOCK_SIZE); // Using RELU block size for reglu
-            sycl_parallel_for(main_stream,
+            main_stream->parallel_for(
                     sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_RELU_BLOCK_SIZE)), sycl::range<1>(SYCL_RELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
                 gated_op_fused_reglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });
@@ -989,7 +989,7 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten
     ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div((uint32_t)k, SYCL_SILU_BLOCK_SIZE); // Using SILU block size for swiglu
-            sycl_parallel_for(main_stream,
+            main_stream->parallel_for(
                     sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_SILU_BLOCK_SIZE)), sycl::range<1>(SYCL_SILU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
                 gated_op_fused_swiglu(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });
@@ -1000,7 +1000,7 @@ static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_
     ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
-            sycl_parallel_for(main_stream,
+            main_stream->parallel_for(
                     sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
                 gated_op_fused_geglu_erf(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });
@@ -1011,7 +1011,7 @@ static inline void ggml_sycl_op_geglu_quick(ggml_backend_sycl_context & ctx, ggm
     ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
             const uint32_t num_blocks = ceil_div(k, SYCL_GELU_BLOCK_SIZE);
-            sycl_parallel_for(main_stream,
+            main_stream->parallel_for(
                     sycl::nd_range<1>((num_blocks * sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), sycl::range<1>(SYCL_GELU_BLOCK_SIZE)), [=](sycl::nd_item<1> item_ct1) {
                 gated_op_fused_geglu_quick(x_ptr, g_ptr, dst_ptr, k, n, o0, o1, item_ct1);
             });

ggml/src/ggml-sycl/getrows.cpp

@@ -118,10 +118,12 @@ static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *sr
 
     GGML_ASSERT(ne00 % 2 == 0);
 
-    sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
-        k_get_rows<qk, qr, dq>(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, s3, nb01, nb02, nb03, s10, s11, s12,
-                               item_ct1);
-    });
+    stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             k_get_rows<qk, qr, dq>(
+                                 src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
+                                 s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
+                         });
 
     GGML_UNUSED(dst);
     GGML_UNUSED(ctx);
@@ -154,8 +156,9 @@ static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tens
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        sycl_parallel_for(
-            stream, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
                 k_get_rows_float(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2,
                                  s3, nb01, nb02, nb03, s10, s11, s12, item_ct1);
             });

ggml/src/ggml-sycl/ggml-sycl.cpp

@@ -1746,12 +1746,13 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
     const size_t shared_mem = ncols_pad * sizeof(int);
 
     if (order == GGML_SORT_ORDER_ASC) {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler &cgh) {
             sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
                 sycl::range<1>(shared_mem), cgh);
 
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
                     k_argsort_f32_i32<GGML_SORT_ORDER_ASC>(
                         x, dst, ncols, ncols_pad, item_ct1,
                         dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
@@ -1759,12 +1760,13 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols,
                 });
         });
     } else if (order == GGML_SORT_ORDER_DESC) {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler &cgh) {
             sycl::local_accessor<uint8_t, 1> dpct_local_acc_ct1(
                 sycl::range<1>(shared_mem), cgh);
 
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
                     k_argsort_f32_i32<GGML_SORT_ORDER_DESC>(
                         x, dst, ncols, ncols_pad, item_ct1,
                         dpct_local_acc_ct1.get_multi_ptr<sycl::access::decorated::no>()
@@ -1782,47 +1784,50 @@ static void argmax_f32_i32_sycl(const float *x, int *dst, const int ncols,
     const sycl::range<3> block_nums(1, nrows, 1);
     const size_t shared_mem = 256 * sizeof(float);
 
-    sycl_launch(stream, [&](sycl::handler & cgh) {
+    stream->submit([&](sycl::handler &cgh) {
         sycl::local_accessor<float, 1> shared_data(
             sycl::range<1>(shared_mem/sizeof(float)), cgh);
         sycl::local_accessor<int, 1> shared_indices(
             sycl::range<1>(shared_mem/sizeof(float)), cgh);
 
-        sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
-            const int tid = item_ct1.get_local_id(2);
-            const int row = item_ct1.get_global_id(1);
+        cgh.parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                const int tid = item_ct1.get_local_id(2);
+                const int row = item_ct1.get_global_id(1);
 
-            float max_val = -INFINITY;
-            int   max_idx = -1;
+                float max_val = -INFINITY;
+                int max_idx = -1;
 
-            for (int col = tid; col < ncols; col += 256) {
-                float val = x[row * ncols + col];
-                if (val > max_val) {
-                    max_val = val;
-                    max_idx = col;
-                }
-            }
-
-            shared_data[tid]    = max_val;
-            shared_indices[tid] = max_idx;
-            item_ct1.barrier(sycl::access::fence_space::local_space);
-
-            for (int stride = 256 / 2; stride > 0; stride >>= 1) {
-                if (tid < stride) {
-                    float val1 = shared_data[tid];
-                    float val2 = shared_data[tid + stride];
-                    if (val2 > val1) {
-                        shared_data[tid]    = val2;
-                        shared_indices[tid] = shared_indices[tid + stride];
+                for (int col = tid; col < ncols; col += 256) {
+                    float val = x[row * ncols + col];
+                    if (val > max_val) {
+                        max_val = val;
+                        max_idx = col;
                     }
                 }
-                item_ct1.barrier(sycl::access::fence_space::local_space);
-            }
 
-            if (tid == 0) {
-                dst[row] = shared_indices[0];
-            }
-        });
+                shared_data[tid] = max_val;
+                shared_indices[tid] = max_idx;
+                item_ct1.barrier(sycl::access::fence_space::local_space);
+
+                for (int stride = 256/2; stride > 0; stride >>= 1) {
+                    if (tid < stride) {
+                        float val1 = shared_data[tid];
+                        float val2 = shared_data[tid + stride];
+                        if (val2 > val1) {
+                            shared_data[tid] = val2;
+                            shared_indices[tid] = shared_indices[tid + stride];
+                        }
+                    }
+                    item_ct1.barrier(sycl::access::fence_space::local_space);
+                }
+
+
+                if (tid == 0) {
+                    dst[row] = shared_indices[0];
+                }
+            });
     });
 }
 static void diag_mask_inf_f32_sycl(const float *x, float *dst,
@@ -2895,7 +2900,7 @@ static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, cons
                 void **       ptrs_dst_get = ptrs_dst.get();
                 size_t        nb12_scaled  = src1->type == GGML_TYPE_F16 ? nb12 : s12 * sizeof(sycl::half);
                 size_t        nb13_scaled  = src1->type == GGML_TYPE_F16 ? nb13 : s13 * sizeof(sycl::half);
-                sycl_parallel_for(cgh, sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
                     k_compute_batched_ptrs(src0_f16, src1_f16, dst_ddf, ptrs_src_get, ptrs_dst_get, ne12, ne13, ne23, nb02,
                                            nb03, nb12_scaled, nb13_scaled, nbd2, nbd3, r2, r3, item_ct1);
                 });
@@ -3403,7 +3408,7 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
             {
                 sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne10, max_work_group_size));
                 sycl::range<3> grid_dims(1, n_ids, ids->ne[1]);
-                sycl_launch(stream, [&](sycl::handler & cgh) {
+                stream->submit([&](sycl::handler &cgh) {
                     sycl::local_accessor<int, 0> src1_row_acc(cgh);
 
                     char *__restrict src1_contiguous_get =
@@ -3415,8 +3420,9 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
                     size_t ids_nb_ct6 = ids->nb[1];
                     size_t ids_nb_ct7 = ids->nb[0];
 
-                    sycl_parallel_for(
-                        cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                    cgh.parallel_for(
+                        sycl::nd_range<3>(grid_dims * block_dims, block_dims),
+                        [=](sycl::nd_item<3> item_ct1) {
                             k_copy_src1_to_contiguous(
                                 src1_original, src1_contiguous_get,
                                 dev_cur_src1_row_get,
@@ -3447,14 +3453,15 @@ static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx,
             {
                 sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne0, max_work_group_size));
                 sycl::range<3> grid_dims(1, 1, num_src1_rows);
-                sycl_launch(stream, [&](sycl::handler & cgh) {
+                stream->submit([&](sycl::handler &cgh) {
                     const char *__restrict dst_contiguous_get =
                         dst_contiguous.get();
                     const mmid_row_mapping *__restrict dev_row_mapping_get =
                         dev_row_mapping.get();
 
-                    sycl_parallel_for(
-                        cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                    cgh.parallel_for(
+                        sycl::nd_range<3>(grid_dims * block_dims, block_dims),
+                        [=](sycl::nd_item<3> item_ct1) {
                             k_copy_dst_from_contiguous(dst_original,
                                                        dst_contiguous_get,
                                                        dev_row_mapping_get,
@@ -3570,6 +3577,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_SUB:
             ggml_sycl_sub(ctx, dst);
             break;
+        case GGML_OP_COUNT_EQUAL:
+            ggml_sycl_count_equal(ctx, dst);
+            break;
         case GGML_OP_ACC:
             ggml_sycl_acc(ctx, dst);
             break;
@@ -4063,7 +4073,7 @@ static ggml_backend_i ggml_backend_sycl_interface = {
     /* .graph_compute           = */ ggml_backend_sycl_graph_compute,
     /* .event_record            = */ ggml_backend_sycl_event_record,
     /* .event_wait              = */ ggml_backend_sycl_event_wait,
-    /* .optimize_graph          = */ NULL,
+    /* .graph_optimize          = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_sycl_guid() {
@@ -4349,6 +4359,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_ADD:
         case GGML_OP_ADD1:
         case GGML_OP_SUB:
+        case GGML_OP_COUNT_EQUAL:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
         case GGML_OP_REPEAT:

ggml/src/ggml-sycl/gla.cpp

@@ -11,13 +11,13 @@ static void gated_linear_attn_f32_kernel(const dpct::queue_ptr stream, u_int B,
     const u_int n_seq_tokens = T / B;
     sycl::range<1> block_dims((C / H));
     sycl::range<1> grid_dims((B * H));
-    sycl_launch(stream, [&](sycl::handler & cgh) {
+    stream->submit([&](sycl::handler & cgh) {
         /* local memory accessors*/
         auto _k  = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
         auto _r  = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
         auto _td = sycl::local_accessor<float, 1>(sycl::range<1>(head_size), cgh);
 
-        sycl_parallel_for<1>(cgh, sycl::nd_range<1>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<1> item) {
+        cgh.parallel_for(sycl::nd_range<1>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<1> item) {
             u_int tid = item.get_local_id(0);
             u_int bid = item.get_group(0);
 

ggml/src/ggml-sycl/im2col.cpp

@@ -70,7 +70,7 @@ static void im2col_sycl_internal(const float * x, T * dst, int64_t IW, int64_t I
 
     const int64_t CHW = IC * KH * KW;
 
-    sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * local_range, local_range), [=](sycl::nd_item<3> item_ct1) {
+    stream->parallel_for(sycl::nd_range<3>(block_nums * local_range, local_range), [=](sycl::nd_item<3> item_ct1) {
         im2col_kernel<T>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, CHW, s0, s1,
                          p0, p1, d0, d1, item_ct1);
     });

ggml/src/ggml-sycl/mmq.cpp

@@ -1818,7 +1818,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
@@ -1829,8 +1829,9 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q4_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -1852,7 +1853,7 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_qs_q4_0_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<float, 1> tile_x_d_q4_0_acc_ct1(
@@ -1863,8 +1864,9 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q4_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -1931,7 +1933,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
@@ -1942,8 +1944,9 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q4_1<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -1965,7 +1968,7 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_qs_q4_1_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_1_acc_ct1(
@@ -1976,8 +1979,9 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q4_1<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2044,7 +2048,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
                     sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
@@ -2055,8 +2059,9 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q5_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2078,7 +2083,7 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q5_0_acc_ct1(
                     sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<float, 1> tile_x_d_q5_0_acc_ct1(
@@ -2089,8 +2094,9 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q5_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2157,7 +2163,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
                     sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
@@ -2168,8 +2174,9 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q5_1<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2191,7 +2198,7 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q5_1_acc_ct1(
                     sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_1_acc_ct1(
@@ -2202,8 +2209,9 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q5_1<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2270,7 +2278,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
@@ -2281,8 +2289,9 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q8_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2304,7 +2313,7 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_qs_q8_0_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<float, 1> tile_x_d_q8_0_acc_ct1(
@@ -2315,8 +2324,9 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q8_0<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2383,7 +2393,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
@@ -2396,8 +2406,9 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q2_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2420,7 +2431,7 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q2_K_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q2_K_acc_ct1(
@@ -2433,8 +2444,9 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q2_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2504,7 +2516,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
@@ -2519,8 +2531,9 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q3_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2544,7 +2557,7 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q3_K_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q3_K_acc_ct1(
@@ -2559,8 +2572,9 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q3_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2630,7 +2644,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
@@ -2643,8 +2657,9 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q4_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2667,7 +2682,7 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q4_K_acc_ct1(
                     sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q4_K_acc_ct1(
@@ -2680,8 +2695,9 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q4_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2749,7 +2765,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
                     sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
@@ -2762,8 +2778,9 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q5_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2786,7 +2803,7 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_q5_K_acc_ct1(
                     sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_q5_K_acc_ct1(
@@ -2799,8 +2816,9 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q5_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2868,7 +2886,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
                     sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
@@ -2881,8 +2899,9 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q6_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,
@@ -2905,7 +2924,7 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
             dpct::has_capability_or_fail(stream->get_device(),
                                          {sycl::aspect::fp16});
 
-            sycl_launch(stream, [&](sycl::handler & cgh) {
+            stream->submit([&](sycl::handler &cgh) {
                 sycl::local_accessor<int, 1> tile_x_ql_acc_ct1(
                     sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh);
                 sycl::local_accessor<sycl::half2, 1> tile_x_dm_acc_ct1(
@@ -2918,8 +2937,9 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy,
                 sycl::local_accessor<sycl::half2, 1> tile_y_ds_acc_ct1(
                     sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh);
 
-                sycl_parallel_for(
-                    cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+                cgh.parallel_for(
+                    sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                    [=](sycl::nd_item<3> item_ct1) {
                         mul_mat_q6_K<need_check>(
                             vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y,
                             nrows_dst, item_ct1,

ggml/src/ggml-sycl/mmvq.cpp

@@ -544,12 +544,12 @@ static void reorder_mul_mat_vec_q4_0_q8_1_sycl(const void * vx, const void * vy,
     const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, (block_num_y * WARP_SIZE));
     const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE);
 
-    sycl_launch(stream, [&](sycl::handler & cgh) {
-        sycl_parallel_for(cgh, sycl::nd_range<3>(global_size, workgroup_size),
-                          [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q4_0>>(vx, vy, dst, ncols, nrows,
-                                                                                            nd_item);
-                          });
+    stream->submit([&](sycl::handler & cgh) {
+        cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size),
+                         [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                             mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q4_0>>(vx, vy, dst, ncols, nrows,
+                                                                                           nd_item);
+                         });
     });
 }
 
@@ -561,12 +561,12 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void * vx, const void * vy, float *
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
 
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+        stream->submit([&](sycl::handler & cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                             [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                                 mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
+                                     vx, vy, dst, ncols, nrows, item_ct1);
+                             });
         });
     }
 }
@@ -580,12 +580,17 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
+                                      VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -599,12 +604,17 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
+                                      VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -618,12 +628,17 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
+                                      VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -637,12 +652,17 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
+                                      VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -656,12 +676,17 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
+                                      VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -675,12 +700,17 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
+                                      VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -694,12 +724,17 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
+                                      VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -715,12 +750,12 @@ static void reorder_mul_mat_vec_q4_k_q8_1_sycl(const void * vx, const void * vy,
     const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, block_num_y * WARP_SIZE);
     const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE);
 
-    sycl_launch(stream, [&](sycl::handler & cgh) {
-        sycl_parallel_for(cgh, sycl::nd_range<3>(global_size, workgroup_size),
-                          [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q4_K>>(vx, vy, dst, ncols, nrows,
-                                                                                            nd_item);
-                          });
+    stream->submit([&](sycl::handler & cgh) {
+        cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size),
+                            [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                                mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q4_K>>(vx, vy, dst, ncols,
+                                                                                            nrows, nd_item);
+                            });
     });
 }
 
@@ -734,12 +769,17 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
+                                      VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -754,12 +794,12 @@ static void reorder_mul_mat_vec_q6_k_q8_1_sycl(const void * vx, const void * vy,
     const sycl::range<3> global_size(1, GGML_SYCL_MMV_Y, block_num_y * WARP_SIZE);
     const sycl::range<3> workgroup_size(1, GGML_SYCL_MMV_Y, num_subgroups * WARP_SIZE);
 
-    sycl_launch(stream, [&](sycl::handler & cgh) {
-        sycl_parallel_for(cgh, sycl::nd_range<3>(global_size, workgroup_size),
-                          [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                              mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q6_K>>(vx, vy, dst, ncols, nrows,
-                                                                                            nd_item);
-                          });
+    stream->submit([&](sycl::handler & cgh) {
+        cgh.parallel_for(sycl::nd_range<3>(global_size, workgroup_size),
+                         [=](sycl::nd_item<3> nd_item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                             mul_mat_vec_q_reorder<reorder_vec_dot_q_sycl<GGML_TYPE_Q6_K>>(vx, vy, dst, ncols, nrows,
+                                                                                           nd_item);
+                         });
     });
 }
 static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
@@ -771,12 +811,17 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
-                                      vx, vy, dst, ncols, nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
+                                      VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -791,12 +836,14 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS / 2, block_iq2_xxs, 1>(vx, vy, dst, ncols,
-                                                                                                  nrows, item_ct1);
-                              });
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS/2, block_iq2_xxs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -810,12 +857,14 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS / 2, block_iq2_xs, 1>(vx, vy, dst, ncols,
-                                                                                               nrows, item_ct1);
-                              });
+        stream->submit([&](sycl::handler & cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS/2, block_iq2_xs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -829,12 +878,15 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S / 2, block_iq2_s, 1>(vx, vy, dst, ncols, nrows,
-                                                                                            item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S/2, block_iq2_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -848,12 +900,15 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS / 2, block_iq3_xxs, 1>(vx, vy, dst, ncols,
-                                                                                                  nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS/2, block_iq3_xxs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -867,12 +922,15 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_S / 2, block_iq3_s, 1>(vx, vy, dst, ncols, nrows,
-                                                                                            item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_S/2, block_iq3_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -886,12 +944,15 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(vx, vy, dst, ncols, nrows,
-                                                                                        item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -905,12 +966,14 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(vx, vy, dst, ncols, nrows,
-                                                                                        item_ct1);
-                              });
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -924,12 +987,15 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 2>(vx, vy, dst, ncols, nrows,
-                                                                                             item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 2>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }
@@ -943,12 +1009,15 @@ static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy,
     const sycl::range<3> block_nums(1, 1, block_num_y);
     const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
     {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS / 4, block_iq4_xs, 1>(vx, vy, dst, ncols,
-                                                                                               nrows, item_ct1);
-                              });
+
+        stream->submit([&](sycl::handler &cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                    [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS/4, block_iq4_xs, 1>(
+                            vx, vy, dst, ncols, nrows, item_ct1);
+                    });
         });
     }
 }

ggml/src/ggml-sycl/norm.cpp

@@ -254,13 +254,14 @@ static void norm_f32_sycl(const float * x, float * dst, const int ncols, const i
     GGML_ASSERT(ncols % WARP_SIZE == 0);
     if (ncols < 1024) {
         const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
-                                           nullptr, WARP_SIZE);
-                              });
-        });
+        stream->submit([&](sycl::handler& cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE);
+                });
+            });
     }
     else {
         const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
@@ -271,15 +272,16 @@ static void norm_f32_sycl(const float * x, float * dst, const int ncols, const i
         the limit. To get the device limit, query
         info::device::max_work_group_size. Adjust the work-group size if needed.
         */
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler& cgh) {
             sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
                             sycl::range<1>(work_group_size / WARP_SIZE), cgh);
-            sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
-                                           get_pointer(s_sum_acc_ct1), work_group_size);
-                              });
-        });
+            cgh.parallel_for(
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
+                });
+            });
     }
 }
 
@@ -288,14 +290,18 @@ static void group_norm_f32_sycl(const float* x, float* dst,
     const int ne_elements, queue_ptr stream, int device) {
     if (group_size < 1024) {
         const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler& cgh) {
             const float eps_ct4 = eps;
-            sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  group_norm_f32(x, dst, group_size, ne_elements, eps_ct4, item_ct1, nullptr,
-                                                 WARP_SIZE);
-                              });
-        });
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    group_norm_f32(
+                        x, dst, group_size, ne_elements, eps_ct4, item_ct1,
+                        nullptr, WARP_SIZE);
+                });
+            });
     }
     else {
         const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
@@ -307,18 +313,22 @@ static void group_norm_f32_sycl(const float* x, float* dst,
         info::device::max_work_group_size. Adjust the work-group size if needed.
         */
 
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler& cgh) {
             sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
                 cgh);
 
             const float eps_ct4 = eps;
 
-            sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  group_norm_f32(x, dst, group_size, ne_elements, eps_ct4, item_ct1,
-                                                 get_pointer(s_sum_acc_ct1), work_group_size);
-                              });
-        });
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    group_norm_f32(x, dst, group_size, ne_elements,
+                        eps_ct4, item_ct1,
+                        get_pointer(s_sum_acc_ct1), work_group_size);
+                });
+            });
     }
 }
 
@@ -330,13 +340,14 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const
     const sycl::range<3> global_dims(nsamples, nchannels, nrows);
     if (ncols < 1024) {
         const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
-                                               nullptr, WARP_SIZE);
-                              });
-        });
+        stream->submit([&](sycl::handler& cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, nullptr, WARP_SIZE);
+                });
+            });
     }
     else {
         const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
@@ -347,15 +358,16 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const
         the limit. To get the device limit, query
         info::device::max_work_group_size. Adjust the work-group size if needed.
         */
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler& cgh) {
             sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
                 cgh);
-            sycl_parallel_for(cgh, sycl::nd_range<3>(global_dims * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1,
-                                               get_pointer(s_sum_acc_ct1), work_group_size);
-                              });
-        });
+            cgh.parallel_for(
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    rms_norm_f32(x, dst, ncols, stride_row, stride_channel, stride_sample, eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size);
+                });
+            });
     }
 }
 
@@ -366,12 +378,16 @@ static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols,
     // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
     if (ncols < 1024) {
         const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        sycl_launch(stream, [&](sycl::handler & cgh) {
-            sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  l2_norm_f32(x, dst, ncols, eps, item_ct1, nullptr, WARP_SIZE);
-                              });
-        });
+        stream->submit([&](sycl::handler& cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    l2_norm_f32(x, dst, ncols, eps, item_ct1,
+                        nullptr, WARP_SIZE);
+                });
+            });
     }
     else {
         const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
@@ -382,15 +398,18 @@ static void l2_norm_f32_sycl(const float* x, float* dst, const int ncols,
         the limit. To get the device limit, query
         info::device::max_work_group_size. Adjust the work-group size if needed.
         */
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler& cgh) {
             sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
                 cgh);
-            sycl_parallel_for(cgh, sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
-                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
-                                  l2_norm_f32(x, dst, ncols, eps, item_ct1, get_pointer(s_sum_acc_ct1),
-                                              work_group_size);
-                              });
-        });
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    l2_norm_f32(x, dst, ncols, eps, item_ct1,
+                        get_pointer(s_sum_acc_ct1), work_group_size);
+                });
+            });
     }
 }
 

ggml/src/ggml-sycl/rope.cpp

@@ -232,22 +232,20 @@ static void rope_norm_sycl(const T * x, T * dst, const int ne0, const int ne1, c
         the limit. To get the device limit, query
         info::device::max_work_group_size. Adjust the work-group size if needed.
         */
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) {
-                              rope_norm<T, false>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
-                                                  attn_factor, corr_dims, theta_scale, freq_factors, item_ct1);
-                          });
+        stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            rope_norm<T, false>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims,
+                                theta_scale, freq_factors, item_ct1);
+        });
     } else {
         /*
         DPCT1049:41: The work-group size passed to the SYCL kernel may exceed
         the limit. To get the device limit, query
         info::device::max_work_group_size. Adjust the work-group size if needed.
         */
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) {
-                              rope_norm<T, true>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
-                                                 attn_factor, corr_dims, theta_scale, freq_factors, item_ct1);
-                          });
+        stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            rope_norm<T, true>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims,
+                               theta_scale, freq_factors, item_ct1);
+        });
     }
 }
 
@@ -266,17 +264,15 @@ static void rope_neox_sycl(const T * x, T * dst, const int ne0, const int ne1, c
     dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
 
     if (freq_factors == nullptr) {
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) {
-                              rope_neox<T, false>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
-                                                  attn_factor, corr_dims, theta_scale, freq_factors, item_ct1);
-                          });
+        stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            rope_neox<T, false>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims,
+                                theta_scale, freq_factors, item_ct1);
+        });
     } else {
-        sycl_parallel_for(stream, sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                          [=](sycl::nd_item<3> item_ct1) {
-                              rope_neox<T, true>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor,
-                                                 attn_factor, corr_dims, theta_scale, freq_factors, item_ct1);
-                          });
+        stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            rope_neox<T, true>(x, dst, ne0, ne1, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims,
+                               theta_scale, freq_factors, item_ct1);
+        });
     }
 }
 
@@ -299,12 +295,12 @@ static void rope_multi_sycl(const T * x, T * dst, const int ne0, const int ne1,
     }
     // launch kernel
     if (freq_factors == nullptr) {
-        sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) {
+        stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
             rope_multi<T, false>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
                                   corr_dims, theta_scale, freq_factors, sections, item_ct1);
         });
     } else {
-        sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) {
+        stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
             rope_multi<T, true>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
                                  corr_dims, theta_scale, freq_factors, sections, item_ct1);
         });
@@ -334,12 +330,12 @@ static void rope_vision_sycl(const T * x, T * dst, const int ne0, const int ne1,
     }
     // launch kernel
     if (freq_factors == nullptr) {
-        sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) {
+        stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
             rope_vision<T, false>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
                                   corr_dims, theta_scale, freq_factors, sections, item_ct1);
         });
     } else {
-        sycl_parallel_for(stream, nd_range, [=](sycl::nd_item<3> item_ct1) {
+        stream->parallel_for(nd_range, [=](sycl::nd_item<3> item_ct1) {
             rope_vision<T, true>(x, dst, ne0, ne1, ne2, s1, s2, n_dims, pos, freq_scale, ext_factor, attn_factor,
                                  corr_dims, theta_scale, freq_factors, sections, item_ct1);
         });

ggml/src/ggml-sycl/set_rows.cpp

@@ -48,7 +48,7 @@ static void set_rows_sycl_q(const char * __restrict__ src0_d,
     constexpr int block_size   = 256;
     const int64_t grid_size    = ceil_div(total_blocks, block_size);
 
-    sycl_parallel_for(stream, sycl::nd_range<1>(grid_size * block_size, block_size), [=](sycl::nd_item<1> item_ct1) {
+    stream->parallel_for(sycl::nd_range<1>(grid_size * block_size, block_size), [=](sycl::nd_item<1> item_ct1) {
         const int64_t i = item_ct1.get_global_linear_id();
         if (i >= total_blocks) {
             return;
@@ -129,8 +129,7 @@ static void set_rows_sycl(
     constexpr int block_size = 64;
     const int64_t grid_size = ceil_div(total_elements, block_size);
 
-    sycl_parallel_for(
-        stream,
+    stream->parallel_for(
         sycl::nd_range<1>(grid_size * block_size, block_size),
         [=](sycl::nd_item<1> item_ct1) {
             k_set_rows<TIn, TOut>(

ggml/src/ggml-sycl/softmax.cpp

@@ -127,11 +127,11 @@ static void soft_max_f32_submitter(const float * x, const T * mask, float * dst,
                                    const int nrows_y, const float scale, const float max_bias, const float m0,
                                    const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims,
                                    const size_t n_local_scratch, queue_ptr stream) {
-    sycl_launch(stream, [&](sycl::handler & cgh) {
+    stream->submit([&](sycl::handler &cgh) {
         sycl::local_accessor<float, 1> local_buf_acc(n_local_scratch, cgh);
 
-        sycl_parallel_for(
-            cgh, sycl::nd_range<3>(block_nums * block_dims, block_dims),
+        cgh.parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
             [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                 soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par,
                                                                              nrows_y, scale, max_bias, m0,

ggml/src/ggml-sycl/tsembd.cpp

@@ -21,11 +21,12 @@ static void timestep_embedding_f32(
     int j = item_ct1.get_local_id(2) + item_ct1.get_group(2) * item_ct1.get_local_range(2);
     float * embed_data = (float *)((char *)dst +  i*nb1);
 
-    if (dim % 2 != 0 && j == ((dim + 1) / 2)) {
-        embed_data[dim] = 0.f;
+    int half = dim / 2;
+
+    if (dim % 2 != 0 && j == half) {
+        embed_data[2 * half] = 0.f;
     }
 
-    int half = dim / 2;
     if (j >= half) {
         return;
     }
@@ -45,9 +46,14 @@ static void timestep_embedding_f32_sycl(
     int num_blocks = (half_ceil + SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE - 1) / SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE;
     sycl::range<3> block_dims(1, 1, SYCL_TIMESTEP_EMBEDDING_BLOCK_SIZE);
     sycl::range<3> gridDim(1, ne00, num_blocks);
-    sycl_parallel_for(stream, sycl::nd_range<3>(gridDim * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
-        timestep_embedding_f32(x, dst, nb1, dim, max_period, item_ct1);
-    });
+    stream->parallel_for(
+        sycl::nd_range<3>(
+            gridDim * block_dims, block_dims),
+        [=](sycl::nd_item<3> item_ct1) {
+            timestep_embedding_f32(
+                x, dst, nb1, dim, max_period, item_ct1
+            );
+        });
 }
 
 void ggml_sycl_op_timestep_embedding(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {

ggml/src/ggml-sycl/wkv.cpp

@@ -207,11 +207,12 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
 
     // Submit kernel
     if (C / H == WKV_BLOCK_SIZE) {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler& cgh) {
             sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
 
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(grid_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
                     rwkv_wkv6_f32_kernel<WKV_BLOCK_SIZE>(
                         B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d,
                         item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()
@@ -219,11 +220,12 @@ void ggml_sycl_op_rwkv_wkv6(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
                 });
         });
     } else {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler& cgh) {
             sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
 
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(grid_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
                     rwkv_wkv6_f32_kernel<WKV_BLOCK_SIZE * 2>(
                         B, T, C, H, k_d, v_d, r_d, tf_d, td_d, s_d, dst_d,
                         item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()
@@ -262,11 +264,12 @@ void ggml_sycl_op_rwkv_wkv7(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
 
     // Submit kernel
     if (C / H == WKV_BLOCK_SIZE) {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler& cgh) {
             sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
 
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(grid_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
                     rwkv_wkv7_f32_kernel<WKV_BLOCK_SIZE>(
                         B, T, C, H, r_d, w_d, k_d, v_d, a_d, b_d, s_d, dst_d,
                         item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()
@@ -274,11 +277,12 @@ void ggml_sycl_op_rwkv_wkv7(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
                 });
         });
     } else {
-        sycl_launch(stream, [&](sycl::handler & cgh) {
+        stream->submit([&](sycl::handler& cgh) {
             sycl::local_accessor<float, 1> shared_mem_acc(shared_mem_size, cgh);
 
-            sycl_parallel_for(
-                cgh, sycl::nd_range<3>(grid_dims * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(grid_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1) {
                     rwkv_wkv7_f32_kernel<WKV_BLOCK_SIZE * 2>(
                         B, T, C, H, r_d, w_d, k_d, v_d, a_d, b_d, s_d, dst_d,
                         item_ct1, (float*)shared_mem_acc.get_multi_ptr<sycl::access::decorated::no>().get()

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -5,8 +5,14 @@
 #include "ggml-cpu.h"
 #endif
 
+// See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers-
+#define VULKAN_HPP_DISPATCH_LOADER_DYNAMIC 1
+
 #include <vulkan/vulkan.hpp>
 
+// See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers-
+VULKAN_HPP_DEFAULT_DISPATCH_LOADER_DYNAMIC_STORAGE
+
 #include <algorithm>
 #include <cmath>
 #include <iomanip>
@@ -121,6 +127,8 @@ struct vk_pipeline_struct {
     bool needed {};
     // set to true when the shader has been compiled
     bool compiled {};
+    // number of registers used, extracted from pipeline executable properties
+    uint32_t register_count {};
 };
 
 typedef std::shared_ptr<vk_pipeline_struct> vk_pipeline;
@@ -429,6 +437,8 @@ struct vk_device_struct {
 
     bool coopmat2;
 
+    bool pipeline_executable_properties_support {};
+
     size_t idx;
 
     bool mul_mat_l[GGML_TYPE_COUNT];
@@ -583,7 +593,7 @@ struct vk_device_struct {
     bool disable_fusion;
     bool disable_host_visible_vidmem;
     bool allow_sysmem_fallback;
-    bool disable_optimize_graph;
+    bool disable_graph_optimize;
 
 #ifdef GGML_VULKAN_MEMORY_DEBUG
     std::unique_ptr<vk_memory_logger> memory_logger;
@@ -1221,8 +1231,6 @@ static std::string format_size(size_t size) {
     return oss.str();
 }
 
-static std::mutex log_mutex;
-
 class vk_memory_logger {
 public:
     vk_memory_logger(): total_device(0), total_host(0) {}
@@ -1412,6 +1420,8 @@ struct ggml_backend_vk_buffer_context {
 };
 
 #ifdef GGML_VULKAN_MEMORY_DEBUG
+static std::mutex log_mutex;
+
 void vk_memory_logger::log_allocation(vk_buffer_ref buf_ref, size_t size) {
     std::lock_guard<std::mutex> guard(log_mutex);
     vk_buffer buf = buf_ref.lock();
@@ -1603,6 +1613,20 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
         vk_instance.pfn_vkSetDebugUtilsObjectNameEXT(device->device, &static_cast<VkDebugUtilsObjectNameInfoEXT &>(duoni));
     }
 
+    if (device->pipeline_executable_properties_support) {
+        vk::PipelineExecutableInfoKHR executableInfo;
+        executableInfo.pipeline = pipeline->pipeline;
+
+        auto statistics = device->device.getPipelineExecutableStatisticsKHR(executableInfo);
+        for (auto & s : statistics) {
+            // "Register Count" is reported by NVIDIA drivers.
+            if (strcmp(s.name, "Register Count") == 0) {
+                VK_LOG_DEBUG(pipeline->name << " " << s.name << ": " << s.value.u64 << " registers");
+                pipeline->register_count = (uint32_t)s.value.u64;
+            }
+        }
+    }
+
     {
         std::lock_guard<std::recursive_mutex> guard(device->mutex);
         device->all_pipelines.push_back(pipeline);
@@ -1960,7 +1984,7 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, const std
         }
     }
 
-    if (buf->device_memory == VK_NULL_HANDLE) {
+    if (!buf->device_memory) {
         device->device.destroyBuffer(buf->buffer);
         throw vk::OutOfDeviceMemoryError("No suitable memory type found");
     }
@@ -3600,8 +3624,8 @@ static vk_device ggml_vk_get_device(size_t idx) {
         const char* GGML_VK_ALLOW_SYSMEM_FALLBACK = getenv("GGML_VK_ALLOW_SYSMEM_FALLBACK");
         device->allow_sysmem_fallback = GGML_VK_ALLOW_SYSMEM_FALLBACK != nullptr;
 
-        const char* GGML_VK_DISABLE_OPTIMIZE_GRAPH = getenv("GGML_VK_DISABLE_OPTIMIZE_GRAPH");
-        device->disable_optimize_graph = GGML_VK_DISABLE_OPTIMIZE_GRAPH != nullptr;
+        const char* GGML_VK_DISABLE_GRAPH_OPTIMIZE = getenv("GGML_VK_DISABLE_GRAPH_OPTIMIZE");
+        device->disable_graph_optimize = GGML_VK_DISABLE_GRAPH_OPTIMIZE != nullptr;
 
         bool fp16_storage = false;
         bool fp16_compute = false;
@@ -3610,6 +3634,7 @@ static vk_device ggml_vk_get_device(size_t idx) {
         bool amd_shader_core_properties2 = false;
         bool pipeline_robustness = false;
         bool coopmat2_support = false;
+        bool pipeline_executable_properties_support = false;
         device->coopmat_support = false;
         device->integer_dot_product = false;
         bool bfloat16_support = false;
@@ -3652,6 +3677,8 @@ static vk_device ggml_vk_get_device(size_t idx) {
                        !getenv("GGML_VK_DISABLE_BFLOAT16")) {
                 bfloat16_support = true;
 #endif
+            } else if (strcmp("VK_KHR_pipeline_executable_properties", properties.extensionName) == 0) {
+                pipeline_executable_properties_support = true;
             }
         }
 
@@ -3878,8 +3905,18 @@ static vk_device ggml_vk_get_device(size_t idx) {
             device_extensions.push_back("VK_KHR_shader_integer_dot_product");
         }
 
+        VkPhysicalDevicePipelineExecutablePropertiesFeaturesKHR pep_features {};
+        pep_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_EXECUTABLE_PROPERTIES_FEATURES_KHR;
+        if (pipeline_executable_properties_support) {
+            last_struct->pNext = (VkBaseOutStructure *)&pep_features;
+            last_struct = (VkBaseOutStructure *)&pep_features;
+            device_extensions.push_back("VK_KHR_pipeline_executable_properties");
+        }
+
         vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2);
 
+        device->pipeline_executable_properties_support = pipeline_executable_properties_support;
+
         device->fp16 = device->fp16 && vk12_features.shaderFloat16;
 
 #if defined(VK_KHR_shader_bfloat16)
@@ -4386,8 +4423,8 @@ static void ggml_vk_print_gpu_info(size_t idx) {
 
 static bool ggml_vk_instance_validation_ext_available();
 static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector<vk::ExtensionProperties>& instance_extensions);
-
 static bool ggml_vk_instance_debug_utils_ext_available(const std::vector<vk::ExtensionProperties> & instance_extensions);
+static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev);
 
 static void ggml_vk_instance_init() {
     if (vk_instance_initialized) {
@@ -4395,6 +4432,9 @@ static void ggml_vk_instance_init() {
     }
     VK_LOG_DEBUG("ggml_vk_instance_init()");
 
+    // See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers-
+    VULKAN_HPP_DEFAULT_DISPATCHER.init(vkGetInstanceProcAddr);
+
     uint32_t api_version = vk::enumerateInstanceVersion();
 
     if (api_version < VK_API_VERSION_1_2) {
@@ -4462,6 +4502,9 @@ static void ggml_vk_instance_init() {
 
     vk_perf_logger_enabled = getenv("GGML_VK_PERF_LOGGER") != nullptr;
 
+    // See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers-
+    VULKAN_HPP_DEFAULT_DISPATCHER.init(vk_instance.instance);
+
     std::vector<vk::PhysicalDevice> devices = vk_instance.instance.enumeratePhysicalDevices();
 
     // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan
@@ -4497,7 +4540,7 @@ static void ggml_vk_instance_init() {
             new_driver.pNext = &new_id;
             devices[i].getProperties2(&new_props);
 
-            if (new_props.properties.deviceType == vk::PhysicalDeviceType::eDiscreteGpu) {
+            if ((new_props.properties.deviceType == vk::PhysicalDeviceType::eDiscreteGpu || new_props.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu) && ggml_vk_device_is_supported(devices[i])) {
                 // Check if there are two physical devices corresponding to the same GPU
                 auto old_device = std::find_if(
                     vk_instance.device_indices.begin(),
@@ -4567,7 +4610,7 @@ static void ggml_vk_instance_init() {
             }
         }
 
-        // If no dedicated GPUs found, fall back to the first non-CPU device.
+        // If no GPUs found, fall back to the first non-CPU device.
         // If only CPU devices are available, return without devices.
         if (vk_instance.device_indices.empty()) {
             for (size_t i = 0; i < devices.size(); i++) {
@@ -11871,12 +11914,12 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
 }
 
 // Sort the graph for improved parallelism.
-static void ggml_vk_optimize_graph(ggml_backend_t backend, struct ggml_cgraph * graph)
+static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * graph)
 {
-    VK_LOG_DEBUG("ggml_vk_optimize_graph(" << graph->n_nodes << " nodes)");
+    VK_LOG_DEBUG("ggml_vk_graph_optimize(" << graph->n_nodes << " nodes)");
     ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context;
 
-    if (ctx->device->disable_optimize_graph) {
+    if (ctx->device->disable_graph_optimize) {
         return;
     }
 
@@ -12010,7 +12053,7 @@ static ggml_backend_i ggml_backend_vk_interface = {
     /* .graph_compute           = */ ggml_backend_vk_graph_compute,
     /* .event_record            = */ NULL,
     /* .event_wait              = */ NULL,
-    /* .optimize_graph          = */ ggml_vk_optimize_graph,
+    /* .graph_optimize          = */ ggml_vk_graph_optimize,
 };
 
 static ggml_guid_t ggml_backend_vk_guid() {
@@ -12078,12 +12121,63 @@ void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total
     }
 }
 
+static vk::PhysicalDeviceType ggml_backend_vk_get_device_type(int device_idx) {
+    GGML_ASSERT(device_idx >= 0 && device_idx < (int) vk_instance.device_indices.size());
+
+    vk::PhysicalDevice device = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device_idx]];
+
+    vk::PhysicalDeviceProperties2 props = {};
+    device.getProperties2(&props);
+
+    return props.properties.deviceType;
+}
+
+static std::string ggml_backend_vk_get_device_pci_id(int device_idx) {
+    GGML_ASSERT(device_idx >= 0 && device_idx < (int) vk_instance.device_indices.size());
+
+    vk::PhysicalDevice device = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device_idx]];
+
+    const std::vector<vk::ExtensionProperties> ext_props = device.enumerateDeviceExtensionProperties();
+
+    bool ext_support = false;
+
+    for (const auto& properties : ext_props) {
+        if (strcmp("VK_EXT_pci_bus_info", properties.extensionName) == 0) {
+            ext_support = true;
+            break;
+        }
+    }
+
+    if (!ext_support) {
+        return "";
+    }
+
+    vk::PhysicalDeviceProperties2 props = {};
+    vk::PhysicalDevicePCIBusInfoPropertiesEXT pci_bus_info = {};
+
+    props.pNext = &pci_bus_info;
+
+    device.getProperties2(&props);
+
+    const uint32_t pci_domain = pci_bus_info.pciDomain;
+    const uint32_t pci_bus = pci_bus_info.pciBus;
+    const uint32_t pci_device = pci_bus_info.pciDevice;
+    const uint8_t pci_function = (uint8_t) pci_bus_info.pciFunction; // pci function is between 0 and 7, prevent printf overflow warning
+
+    char pci_bus_id[16] = {};
+    snprintf(pci_bus_id, sizeof(pci_bus_id), "%04x:%02x:%02x.%x", pci_domain, pci_bus, pci_device, pci_function);
+
+    return std::string(pci_bus_id);
+}
+
 //////////////////////////
 
 struct ggml_backend_vk_device_context {
     size_t device;
     std::string name;
     std::string description;
+    bool is_integrated_gpu;
+    std::string pci_bus_id;
 };
 
 static const char * ggml_backend_vk_device_get_name(ggml_backend_dev_t dev) {
@@ -12112,14 +12206,18 @@ static ggml_backend_buffer_type_t ggml_backend_vk_device_get_host_buffer_type(gg
 }
 
 static enum ggml_backend_dev_type ggml_backend_vk_device_get_type(ggml_backend_dev_t dev) {
-    UNUSED(dev);
-    return GGML_BACKEND_DEVICE_TYPE_GPU;
+    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
+
+    return ctx->is_integrated_gpu ? GGML_BACKEND_DEVICE_TYPE_IGPU : GGML_BACKEND_DEVICE_TYPE_GPU;
 }
 
 static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
+    ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
+
     props->name        = ggml_backend_vk_device_get_name(dev);
     props->description = ggml_backend_vk_device_get_description(dev);
     props->type        = ggml_backend_vk_device_get_type(dev);
+    props->device_id   = ctx->pci_bus_id.empty() ? nullptr : ctx->pci_bus_id.c_str();
     ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total);
     props->caps = {
         /* .async                 = */ false,
@@ -12386,8 +12484,8 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                 }
 
                 if (
-                    src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_I32 ||
-                    src0_type == GGML_TYPE_I32 && src1_type == GGML_TYPE_F32
+                    (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_I32) ||
+                    (src0_type == GGML_TYPE_I32 && src1_type == GGML_TYPE_F32)
                 ) {
                     return true;
                 }
@@ -12552,6 +12650,8 @@ static ggml_backend_dev_t ggml_backend_vk_reg_get_device(ggml_backend_reg_t reg,
                 ctx->device = i;
                 ctx->name = GGML_VK_NAME + std::to_string(i);
                 ctx->description = desc;
+                ctx->is_integrated_gpu = ggml_backend_vk_get_device_type(i) == vk::PhysicalDeviceType::eIntegratedGpu;
+                ctx->pci_bus_id = ggml_backend_vk_get_device_pci_id(i);
                 devices.push_back(new ggml_backend_device {
                     /* .iface   = */ ggml_backend_vk_device_i,
                     /* .reg     = */ reg,
@@ -12638,6 +12738,20 @@ static bool ggml_vk_instance_debug_utils_ext_available(
     UNUSED(instance_extensions);
 }
 
+static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev) {
+    VkPhysicalDeviceFeatures2 device_features2;
+    device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2;
+
+    VkPhysicalDeviceVulkan11Features vk11_features;
+    vk11_features.pNext = nullptr;
+    vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES;
+    device_features2.pNext = &vk11_features;
+
+    vkGetPhysicalDeviceFeatures2(vkdev, &device_features2);
+
+    return vk11_features.storageBuffer16BitAccess;
+}
+
 static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch) {
     switch (props.vendorID) {
     case VK_VENDOR_ID_INTEL:
@@ -13038,16 +13152,16 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
     } else if (tensor->op == GGML_OP_IM2COL_3D) {
         const int32_t s0 = tensor->op_params[0];
         const int32_t s1 = tensor->op_params[1];
-        const int32_t s1 = tensor->op_params[2];
+        const int32_t s2 = tensor->op_params[2];
         const int32_t p0 = tensor->op_params[3];
         const int32_t p1 = tensor->op_params[4];
-        const int32_t p1 = tensor->op_params[5];
+        const int32_t p2 = tensor->op_params[5];
         const int32_t d0 = tensor->op_params[6];
         const int32_t d1 = tensor->op_params[7];
-        const int32_t d1 = tensor->op_params[8];
+        const int32_t d2 = tensor->op_params[8];
         const int32_t IC = tensor->op_params[9];
 
-        tensor_clone = ggml_im2col(ggml_ctx, src_clone[0], src_clone[1], IC, s0, s1, s2, p0, p1, p2, d0, d1, d2, tensor->type);
+        tensor_clone = ggml_im2col_3d(ggml_ctx, src_clone[0], src_clone[1], IC, s0, s1, s2, p0, p1, p2, d0, d1, d2, tensor->type);
     } else if (tensor->op == GGML_OP_TIMESTEP_EMBEDDING) {
         const int32_t dim = tensor->op_params[0];
         const int32_t max_period = tensor->op_params[1];

ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_s.comp

@@ -29,7 +29,7 @@ void main() {
         uint qs = data_a[ib].qs[4 * ib32 + l];
         const uint8_t sign = data_a[ib].qs[QUANT_K / 8 + 4 * ib32 + l];
         qs |= (qh << (8 - 2 * l)) & 0x300;
-        const uvec2 grid = iq2s_grid[qs & 511];
+        const uvec2 grid = iq2s_grid[qs];
         const u8vec4 grid0 = unpack8(grid.x);
         const u8vec4 grid1 = unpack8(grid.y);
         data_b[b_idx + 8 * l + 0] = D_TYPE(db[l/2] * grid0.x * ((sign & 1) != 0 ? -1.0 : 1.0));

ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xxs.comp

@@ -33,7 +33,8 @@ void main() {
     [[unroll]] for (uint l = 0; l < 4; ++l) {
         const uint sign7 = bitfieldExtract(signscale, 7 * int(l), 7);
         const uint sign8 = sign7 | (bitCount(sign7) << 7); // parity bit
-        const uvec2 grid = iq2xxs_grid[data_a[ib].qs[8 * is + l]];
+        const uint qs = data_a[ib].qs[8 * is + l];
+        const uvec2 grid = iq2xxs_grid[qs];
         const u8vec4 grid0 = unpack8(grid.x);
         const u8vec4 grid1 = unpack8(grid.y);
         data_b[b_idx + 8 * l + 0] = D_TYPE(db * grid0.x * ((sign8 & 1) != 0 ? -1.0 : 1.0));

ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_s.comp

@@ -22,15 +22,16 @@ void main() {
     const uint b_idx = 256 * ib + 32 * is;
 
     const float d = float(data_a[ib].d);
-    const float db = d * (1 + 2 * ((data_a[ib].scales[is] >> (4 * (is % 2))) & 0xf));
+    const float db = d * (1 + 2 * ((data_a[ib].scales[is / 2] >> (4 * (is % 2))) & 0xf));
 
     // We must produce 32 values using 4 sign bytes, 1 qh byte, 8 qs bytes.
     uint qh = data_a[ib].qh[is];
     [[unroll]] for (uint l = 0; l < 8; ++l) {
-        uint qs = data_a[ib].qs[8 * is + l];
-        uint gidx = qs | ((qh << (8 - l)) & 256);
-        uint8_t signs = data_a[ib].signs[8 * is + l / 2] >> (4 * (l & 1));
-        u8vec4 grid = unpack8(iq3s_grid[gidx]);
+        const uint iqs = 8 * is + l;
+        const uint qs = data_a[ib].qs[iqs];
+        const uint gidx = qs | ((qh << (8 - l)) & 256);
+        const uint8_t signs = data_a[ib].signs[iqs / 2] >> (4 * (l & 1));
+        const u8vec4 grid = unpack8(iq3s_grid[gidx]);
         data_b[b_idx + 4 * l + 0] = D_TYPE(db * grid.x * ((signs & 1) != 0 ? -1.0 : 1.0));
         data_b[b_idx + 4 * l + 1] = D_TYPE(db * grid.y * ((signs & 2) != 0 ? -1.0 : 1.0));
         data_b[b_idx + 4 * l + 2] = D_TYPE(db * grid.z * ((signs & 4) != 0 ? -1.0 : 1.0));

ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_xxs.comp

@@ -35,8 +35,10 @@ void main() {
         const uint sign7 = bitfieldExtract(signscale, 7 * int(l), 7);
         // Restore parity bit.
         const uint sign8 = sign7 | (bitCount(sign7) << 7);
-        const u8vec4 grid0 = unpack8(iq3xxs_grid[data_a[ib].qs[8 * is + 2 * l]]);
-        const u8vec4 grid1 = unpack8(iq3xxs_grid[data_a[ib].qs[8 * is + 2 * l + 1]]);
+        const uint qs0 = data_a[ib].qs[8 * is + 2 * l];
+        const uint qs1 = data_a[ib].qs[8 * is + 2 * l + 1];
+        const u8vec4 grid0 = unpack8(iq3xxs_grid[qs0]);
+        const u8vec4 grid1 = unpack8(iq3xxs_grid[qs1]);
         data_b[b_idx + 8 * l + 0] = D_TYPE(db * grid0.x * ((sign8 & 1) != 0 ? -1.0 : 1.0));
         data_b[b_idx + 8 * l + 1] = D_TYPE(db * grid0.y * ((sign8 & 2) != 0 ? -1.0 : 1.0));
         data_b[b_idx + 8 * l + 2] = D_TYPE(db * grid0.z * ((sign8 & 4) != 0 ? -1.0 : 1.0));

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp

@@ -183,6 +183,8 @@ void load_row_ids(uint expert_idx, bool nei0_is_pow2, uint ic) {
 shared ACC_TYPE coopmat_stage[TM * TN * NUM_WARPS];
 #endif
 
+#include "mul_mm_funcs.comp"
+
 void main() {
 #ifdef NEEDS_INIT_IQ_SHMEM
     init_iq_shmem(gl_WorkGroupSize);
@@ -310,550 +312,13 @@ void main() {
 
     for (uint block = start_k; block < end_k; block += BK) {
         [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) {
-
-#if defined(DATA_A_F32) || defined(DATA_A_F16)
-#if LOAD_VEC_A == 8
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-            A_TYPE32 aa = A_TYPE32(data_a[idx]);
-            buf_a[buf_idx    ] = FLOAT_TYPE(aa[0].x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(aa[0].y);
-            buf_a[buf_idx + 2] = FLOAT_TYPE(aa[0].z);
-            buf_a[buf_idx + 3] = FLOAT_TYPE(aa[0].w);
-            buf_a[buf_idx + 4] = FLOAT_TYPE(aa[1].x);
-            buf_a[buf_idx + 5] = FLOAT_TYPE(aa[1].y);
-            buf_a[buf_idx + 6] = FLOAT_TYPE(aa[1].z);
-            buf_a[buf_idx + 7] = FLOAT_TYPE(aa[1].w);
-#elif LOAD_VEC_A == 4
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-            A_TYPE32 aa = A_TYPE32(data_a[idx]);
-            buf_a[buf_idx    ] = FLOAT_TYPE(aa.x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(aa.y);
-            buf_a[buf_idx + 2] = FLOAT_TYPE(aa.z);
-            buf_a[buf_idx + 3] = FLOAT_TYPE(aa.w);
-#else
-            if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) {
-                buf_a[(loadc_a + l) * SHMEM_STRIDE + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]);
-            } else {
-                buf_a[(loadc_a + l) * SHMEM_STRIDE + loadr_a] = FLOAT_TYPE(0.0f);
-            }
-#endif
-#elif defined(DATA_A_BF16)
-#if LOAD_VEC_A == 4
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-            buf_a[buf_idx    ] = TO_FLOAT_TYPE(data_a[idx].x);
-            buf_a[buf_idx + 1] = TO_FLOAT_TYPE(data_a[idx].y);
-            buf_a[buf_idx + 2] = TO_FLOAT_TYPE(data_a[idx].z);
-            buf_a[buf_idx + 3] = TO_FLOAT_TYPE(data_a[idx].w);
-#else
-            if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) {
-                buf_a[(loadc_a + l) * SHMEM_STRIDE + loadr_a] = TO_FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]);
-            } else {
-                buf_a[(loadc_a + l) * SHMEM_STRIDE + loadr_a] = TO_FLOAT_TYPE(uint16_t(0));
-            }
-#endif
-#elif defined(DATA_A_Q4_0)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + 4 * loadr_a;
-
-            const uint ib = idx / 4;
-            const uint iqs = idx & 0x03;
-
-            const float d = float(data_a_packed16[ib].d);
-            const uint vui = uint(data_a_packed16[ib].qs[2*iqs]) | (uint(data_a_packed16[ib].qs[2*iqs + 1]) << 16);
-            const vec4 v0 = (vec4(unpack8(vui & 0x0F0F0F0F)) - 8.0f) * d;
-            const vec4 v1 = (vec4(unpack8((vui >> 4) & 0x0F0F0F0F)) - 8.0f) * d;
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(v0.x);
-            buf_a[buf_idx + 1 ] = FLOAT_TYPE(v0.y);
-            buf_a[buf_idx + 2 ] = FLOAT_TYPE(v0.z);
-            buf_a[buf_idx + 3 ] = FLOAT_TYPE(v0.w);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(v1.x);
-            buf_a[buf_idx + 17] = FLOAT_TYPE(v1.y);
-            buf_a[buf_idx + 18] = FLOAT_TYPE(v1.z);
-            buf_a[buf_idx + 19] = FLOAT_TYPE(v1.w);
-#elif defined(DATA_A_Q4_1)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + 4 * loadr_a;
-
-            const uint ib = idx / 4;
-            const uint iqs = idx & 0x03;
-
-            const float d = float(data_a_packed16[ib].d);
-            const float m = float(data_a_packed16[ib].m);
-            const uint vui = uint(data_a_packed16[ib].qs[2*iqs]) | (uint(data_a_packed16[ib].qs[2*iqs + 1]) << 16);
-            const vec4 v0 = vec4(unpack8(vui & 0x0F0F0F0F)) * d + m;
-            const vec4 v1 = vec4(unpack8((vui >> 4) & 0x0F0F0F0F)) * d + m;
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(v0.x);
-            buf_a[buf_idx + 1 ] = FLOAT_TYPE(v0.y);
-            buf_a[buf_idx + 2 ] = FLOAT_TYPE(v0.z);
-            buf_a[buf_idx + 3 ] = FLOAT_TYPE(v0.w);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(v1.x);
-            buf_a[buf_idx + 17] = FLOAT_TYPE(v1.y);
-            buf_a[buf_idx + 18] = FLOAT_TYPE(v1.z);
-            buf_a[buf_idx + 19] = FLOAT_TYPE(v1.w);
-#elif defined(DATA_A_Q5_0)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + 2 * loadr_a;
-
-            const uint ib = idx / 8;
-            const uint iqs = idx & 0x07;
-
-            const float d = float(data_a_packed16[ib].d);
-            const uint uint_qh = uint(data_a_packed16[ib].qh[1]) << 16 | uint(data_a_packed16[ib].qh[0]);
-            const ivec2 qh0 = ivec2(((uint_qh >> 2*iqs) << 4) & 0x10, (uint_qh >> (2*iqs + 12)) & 0x10);
-            const ivec2 qh1 = ivec2(((uint_qh >> (2*iqs + 1)) << 4) & 0x10, (uint_qh >> (2*iqs + 13)) & 0x10);
-
-            const uint vui = uint(data_a_packed16[ib].qs[iqs]);
-            const vec4 v = (vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y) - 16.0f) * d;
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 1 ] = FLOAT_TYPE(v.z);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
-            buf_a[buf_idx + 17] = FLOAT_TYPE(v.w);
-#elif defined(DATA_A_Q5_1)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + 2 * loadr_a;
-
-            const uint ib = idx / 8;
-            const uint iqs = idx & 0x07;
-
-            const float d = float(data_a_packed16[ib].d);
-            const float m = float(data_a_packed16[ib].m);
-            const uint uint_qh = data_a_packed16[ib].qh;
-            const ivec2 qh0 = ivec2(((uint_qh >> 2*iqs) << 4) & 0x10, (uint_qh >> (2*iqs + 12)) & 0x10);
-            const ivec2 qh1 = ivec2(((uint_qh >> (2*iqs + 1)) << 4) & 0x10, (uint_qh >> (2*iqs + 13)) & 0x10);
-
-            const uint vui = uint(data_a_packed16[ib].qs[iqs]);
-            const vec4 v = vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y) * d + m;
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 1 ] = FLOAT_TYPE(v.z);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
-            buf_a[buf_idx + 17] = FLOAT_TYPE(v.w);
-#elif defined(DATA_A_Q8_0)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 8;
-            const uint iqs = idx & 0x07;
-
-            const float d = float(data_a_packed16[ib].d);
-            const i8vec2 v0 = unpack8(int32_t(data_a_packed16[ib].qs[2*iqs])).xy; // vec4 used due to #12147
-            const i8vec2 v1 = unpack8(int32_t(data_a_packed16[ib].qs[2*iqs + 1])).xy;
-            const vec4 v = vec4(v0.x, v0.y, v1.x, v1.y) * d;
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
-            buf_a[buf_idx + 2] = FLOAT_TYPE(v.z);
-            buf_a[buf_idx + 3] = FLOAT_TYPE(v.w);
-#elif defined(DATA_A_Q2_K)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                         // 2 values per idx
-            const uint iqs = idx % 128;                        // 0..127
-
-            const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30
-            const uint scalesi = iqs / 8;                      // 0..15
-            const uint qsshift = ((iqs % 64) / 16) * 2;        // 0,2,4,6
-
-            const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]);
-            const uint scales = data_a[ib].scales[scalesi];
-            const vec2 d = vec2(data_a[ib].d);
-
-            const vec2 v = d.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - d.y * float(scales >> 4);
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
-#elif defined(DATA_A_Q3_K)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                   // 2 values per idx
-            const uint iqs = idx % 128;                  // 0..127
-
-            const uint n = iqs / 64;                     // 0,1
-            const uint qsi = n * 32 + (iqs % 16) * 2;    // 0,2,4..62
-            const uint hmi =          (iqs % 16) * 2;    // 0,2,4..30
-            const uint j = (iqs % 64) / 4;               // 0..3
-            const uint is = iqs / 8;                     // 0..15
-            const uint halfsplit = ((iqs % 64) / 16);    // 0,1,2,3
-            const uint qsshift = halfsplit * 2;          // 0,2,4,6
-            const uint m = 1 << (4 * n + halfsplit);     // 1,2,4,8,16,32,64,128
-
-            const int8_t us = int8_t(((data_a[ib].scales[is % 8] >> (4 * int(is / 8))) & 0xF)
-                                  | (((data_a[ib].scales[8 + (is % 4)] >> (2 * int(is / 4))) & 3) << 4));
-            const float dl = float(data_a[ib].d) * float(us - 32);
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi    ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi    ] & m) != 0) ? 0 : 4)));
-            buf_a[buf_idx + 1] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[ib].hmask[hmi + 1] & m) != 0) ? 0 : 4)));
-#elif defined(DATA_A_Q4_K)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                 // 2 values per idx
-            const uint iqs = idx % 128;                // 0..127
-
-            const uint n = iqs / 32;                   // 0,1,2,3
-            const uint b = (iqs % 32) / 16;            // 0,1
-            const uint is = 2 * n + b;                 // 0..7
-            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
-
-            const vec2 loadd = vec2(data_a[ib].d);
-
-            const uint scidx0 = (is < 4) ? is : (is + 4);
-            const uint scidx1 = (is < 4) ? is : (is - 4);
-            const uint scidxmask1 = (is < 4) ? 0x30 : 0xC0;
-            const uint scidxshift1 = (is < 4) ? 0 : 2;
-            const uint mbidx0 = is + 4;
-            const uint mbidx1 = (is < 4) ? is + 4 : is;
-            const uint mbidxmask0 = (is < 4) ? 0xF : 0xF0;
-            const uint mbidxshift0 = (is < 4) ? 0 : 4;
-            const uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
-            const uint mbidxshift1 = (is < 4) ? 0 : 2;
-
-            const uint8_t sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
-            const uint8_t mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
-
-            const float d = loadd.x * sc;
-            const float m = -loadd.y * mbyte;
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF), m));
-            buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF), m));
-#elif defined(DATA_A_Q5_K)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                 // 2 values per idx
-            const uint iqs = idx % 128;                // 0..127
-
-            const uint n = iqs / 32;                   // 0,1,2,3
-            const uint b = (iqs % 32) / 16;            // 0,1
-            const uint is = 2 * n + b;                 // 0..7
-            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
-            const uint qhi = (iqs % 16) * 2;           // 0,2,4..30
-
-            const uint8_t hm = uint8_t(1 << (iqs / 16));
-
-            const vec2 loadd = vec2(data_a[ib].d);
-
-            const uint scidx0 = (is < 4) ? is : (is + 4);
-            const uint scidx1 = (is < 4) ? is : (is - 4);
-            const uint scidxmask1 = (is < 4) ? 0x30 : 0xC0;
-            const uint scidxshift1 = (is < 4) ? 0 : 2;
-            const uint mbidx0 = is + 4;
-            const uint mbidx1 = (is < 4) ? is + 4 : is;
-            const uint mbidxmask0 = (is < 4) ? 0xF : 0xF0;
-            const uint mbidxshift0 = (is < 4) ? 0 : 4;
-            const uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
-            const uint mbidxshift1 = (is < 4) ? 0 : 2;
-
-            const uint8_t sc    = uint8_t((data_a[ib].scales[scidx0] & 0xF)                         | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
-            const uint8_t mbyte = uint8_t(((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
-
-            const float d = loadd.x * sc;
-            const float m = -loadd.y * mbyte;
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi    ] & hm) != 0 ? 16 : 0), m));
-            buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0), m));
-#elif defined(DATA_A_Q6_K)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                  // 2 values per idx
-            const uint iqs = idx % 128;                 // 0..127
-
-            const uint n = iqs / 64;                    // 0,1
-            const uint b = (iqs % 64) / 32;             // 0,1
-            const uint is_b = (iqs % 16) / 8;           // 0,1
-            const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
-            const uint is = 8 * n + qhshift + is_b;     // 0..15
-            const uint qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
-            const uint qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
-
-            const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]);
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32));
-            buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));
-#elif defined(DATA_A_IQ1_S)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 32;                  // 8 values per idx
-            const uint ib32 = (idx % 32) / 4;         // 0..7
-            const uint ib8 = idx % 32;
-
-            const float d = float(data_a[ib].d);
-            const uint qh = data_a[ib].qh[ib32];
-            const uint qs = data_a[ib].qs[ib8];
-            const float dl = d * (2 * bitfieldExtract(qh, 12, 3) + 1);
-            const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
-            const int16_t grid = int16_t(iq1s_grid[qs | (bitfieldExtract(qh, 3 * int(ib8 & 3), 3) << 8)]);
-
-            [[unroll]] for (int k = 0; k < 8; ++k) {
-                buf_a[buf_idx + k] = FLOAT_TYPE(dl * (bitfieldExtract(grid, 2 * k, 2) + delta));
-            }
-#elif defined(DATA_A_IQ1_M)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 32;  // 8 values per idx
-            const uint ib8 = idx % 32;
-            const uint ib16 = ib8 / 2;
-
-            const uint16_t[4] scales = data_a[ib].scales;
-            const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12;
-            const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x);
-            const uint sc = scales[ib8 / 8];
-            const uint qs = data_a[ib].qs[ib8];
-            const uint qh = data_a[ib].qh[ib16] >> (4 * (ib8 & 1));
-            const float dl = d * (2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1);
-            const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA;
-            const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]);
-
-            [[unroll]] for (int k = 0; k < 8; ++k) {
-                buf_a[buf_idx + k] = FLOAT_TYPE(dl * (bitfieldExtract(grid, 2 * k, 2) + delta));
-            }
-#elif defined(DATA_A_IQ2_XXS)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 32;                 // 8 values per idx
-            const uint ib32 = (idx % 32) / 4;         // 0..7
-            const uint ib8 = idx % 4;
-
-            const float d = float(data_a[ib].d);
-            const uint qs = data_a[ib].qs[8 * ib32 + ib8];
-            const uint signs = pack32(u8vec4(
-                data_a[ib].qs[8*ib32 + 4],
-                data_a[ib].qs[8*ib32 + 5],
-                data_a[ib].qs[8*ib32 + 6],
-                data_a[ib].qs[8*ib32 + 7]
-            ));
-            const FLOAT_TYPE db = FLOAT_TYPE(d * 0.25 * (0.5 + (signs >> 28)));
-            const uint32_t sign7 = bitfieldExtract(signs, 7 * int(ib8), 7);
-            const uint sign = sign7 | (bitCount(sign7) << 7);
-            const uvec2 grid = iq2xxs_grid[qs];
-            const vec4 grid0 = vec4(unpack8(grid.x));
-            const vec4 grid1 = vec4(unpack8(grid.y));
-
-            buf_a[buf_idx    ] = db * FLOAT_TYPE((sign &   1) != 0 ? -grid0.x : grid0.x);
-            buf_a[buf_idx + 1] = db * FLOAT_TYPE((sign &   2) != 0 ? -grid0.y : grid0.y);
-            buf_a[buf_idx + 2] = db * FLOAT_TYPE((sign &   4) != 0 ? -grid0.z : grid0.z);
-            buf_a[buf_idx + 3] = db * FLOAT_TYPE((sign &   8) != 0 ? -grid0.w : grid0.w);
-            buf_a[buf_idx + 4] = db * FLOAT_TYPE((sign &  16) != 0 ? -grid1.x : grid1.x);
-            buf_a[buf_idx + 5] = db * FLOAT_TYPE((sign &  32) != 0 ? -grid1.y : grid1.y);
-            buf_a[buf_idx + 6] = db * FLOAT_TYPE((sign &  64) != 0 ? -grid1.z : grid1.z);
-            buf_a[buf_idx + 7] = db * FLOAT_TYPE((sign & 128) != 0 ? -grid1.w : grid1.w);
-#elif defined(DATA_A_IQ2_XS)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 32;            // 8 values per idx
-            const uint ib32 = (idx % 32) / 4;    // 0..7
-            const uint ib8 = idx % 4;            // 0..3
-
-            const float d = float(data_a[ib].d);
-            const uint scale = (data_a[ib].scales[ib32] >> (2 * (ib8 & 2))) & 0xf;
-            const FLOAT_TYPE db = FLOAT_TYPE(d * 0.25 * (0.5 + scale));
-            const uint qs = data_a[ib].qs[4 * ib32 + ib8];
-            const uint sign7 = qs >> 9;
-            const uint sign = sign7 | (bitCount(sign7) << 7);
-            const uvec2 grid = iq2xs_grid[qs & 511];
-            const vec4 grid0 = vec4(unpack8(grid.x));
-            const vec4 grid1 = vec4(unpack8(grid.y));
-
-            buf_a[buf_idx    ] = db * FLOAT_TYPE((sign &   1) != 0 ? -grid0.x : grid0.x);
-            buf_a[buf_idx + 1] = db * FLOAT_TYPE((sign &   2) != 0 ? -grid0.y : grid0.y);
-            buf_a[buf_idx + 2] = db * FLOAT_TYPE((sign &   4) != 0 ? -grid0.z : grid0.z);
-            buf_a[buf_idx + 3] = db * FLOAT_TYPE((sign &   8) != 0 ? -grid0.w : grid0.w);
-            buf_a[buf_idx + 4] = db * FLOAT_TYPE((sign &  16) != 0 ? -grid1.x : grid1.x);
-            buf_a[buf_idx + 5] = db * FLOAT_TYPE((sign &  32) != 0 ? -grid1.y : grid1.y);
-            buf_a[buf_idx + 6] = db * FLOAT_TYPE((sign &  64) != 0 ? -grid1.z : grid1.z);
-            buf_a[buf_idx + 7] = db * FLOAT_TYPE((sign & 128) != 0 ? -grid1.w : grid1.w);
-#elif defined(DATA_A_IQ2_S)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 32;  // 8 values per idx
-            const uint ib8 = idx % 32; // 0..31
-            const uint ib32 = ib8 / 4; // 0..7
-
-            const uint scale = (data_a[ib].scales[ib32] >> (2 * (ib8 & 2))) & 0xf;
-            const uint qs = data_a[ib].qs[ib8];
-            const uint qh = data_a[ib].qh[ib32];
-            const uint qhshift = 2 * (ib8 % 4);
-            const uint sign = data_a[ib].qs[QUANT_K / 8 + ib8];
-
-            const float d = float(data_a[ib].d);
-            const FLOAT_TYPE db = FLOAT_TYPE(d * 0.25 * (0.5 + scale));
-            const uvec2 grid = iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)];
-            const vec4 grid0 = vec4(unpack8(grid.x));
-            const vec4 grid1 = vec4(unpack8(grid.y));
-
-            buf_a[buf_idx    ] = db * FLOAT_TYPE((sign &   1) != 0 ? -grid0.x : grid0.x);
-            buf_a[buf_idx + 1] = db * FLOAT_TYPE((sign &   2) != 0 ? -grid0.y : grid0.y);
-            buf_a[buf_idx + 2] = db * FLOAT_TYPE((sign &   4) != 0 ? -grid0.z : grid0.z);
-            buf_a[buf_idx + 3] = db * FLOAT_TYPE((sign &   8) != 0 ? -grid0.w : grid0.w);
-            buf_a[buf_idx + 4] = db * FLOAT_TYPE((sign &  16) != 0 ? -grid1.x : grid1.x);
-            buf_a[buf_idx + 5] = db * FLOAT_TYPE((sign &  32) != 0 ? -grid1.y : grid1.y);
-            buf_a[buf_idx + 6] = db * FLOAT_TYPE((sign &  64) != 0 ? -grid1.z : grid1.z);
-            buf_a[buf_idx + 7] = db * FLOAT_TYPE((sign & 128) != 0 ? -grid1.w : grid1.w);
-#elif defined(DATA_A_IQ3_XXS)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 64;            // 4 values per idx
-            const uint iqs = idx % 64;           // 0..63
-            const uint is = QUANT_K / 4 + 4 * (iqs / 8); // 8 values
-
-            const float d = float(data_a[ib].d);
-            const uint qs = data_a[ib].qs[iqs];
-            const uint signs = pack32(u8vec4(
-                data_a[ib].qs[is+0],
-                data_a[ib].qs[is+1],
-                data_a[ib].qs[is+2],
-                data_a[ib].qs[is+3]
-            ));
-            const float db = d * 0.5 * (0.5 + (signs >> 28));
-            const uint32_t sign7 = bitfieldExtract(signs, 7 * (int(iqs / 2) % 4), 7);
-            const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (4 * (idx % 2));
-            const uint grid = iq3xxs_grid[qs];
-            const vec4 v = db * vec4(unpack8(grid));
-
-            buf_a[buf_idx    ] = FLOAT_TYPE((sign &   1) != 0 ? -v.x : v.x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE((sign &   2) != 0 ? -v.y : v.y);
-            buf_a[buf_idx + 2] = FLOAT_TYPE((sign &   4) != 0 ? -v.z : v.z);
-            buf_a[buf_idx + 3] = FLOAT_TYPE((sign &   8) != 0 ? -v.w : v.w);
-#elif defined(DATA_A_IQ3_S)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 64;            // 4 values per idx
-            const uint iqs = idx % 64;           // 0..63
-            const uint iqh = iqs / 8;
-
-            const float d = float(data_a[ib].d);
-            const uint qs = data_a[ib].qs[iqs];
-            const uint qh = data_a[ib].qh[iqh];
-            const int8_t sign = int8_t(data_a[ib].signs[iqs / 2] >> (4 * (idx % 2)));
-            const uint scale = data_a[ib].scales[iqs / 16];
-            const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(sign << 1, sign)));
-            const float db = d * (1 + 2 * ((scale >> (4 * (iqh & 1))) & 0xf));
-            const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)];
-            const vec4 v = db * vec4(unpack8(grid));
-
-            buf_a[buf_idx    ] = FLOAT_TYPE((sign &   1) != 0 ? -v.x : v.x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE((sign &   2) != 0 ? -v.y : v.y);
-            buf_a[buf_idx + 2] = FLOAT_TYPE((sign &   4) != 0 ? -v.z : v.z);
-            buf_a[buf_idx + 3] = FLOAT_TYPE((sign &   8) != 0 ? -v.w : v.w);
-#elif defined(DATA_A_IQ4_XS)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + loadr_a * LOAD_VEC_A;
-
-            const uint ib = idx / 128;                  // 2 values per idx
-            const uint ib32 = (idx % 128) / 16;         // 0..7
-            const uint iq = 16 * ib32 + 2 * (idx % 8);
-
-            const uint sl = (data_a[ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF;
-            const uint sh = ((data_a[ib].scales_h) >> (2 * ib32)) & 3;
-            const uint qshift = (idx & 8) >> 1;
-            u8vec2 qs = u8vec2(data_a[ib].qs[iq], data_a[ib].qs[iq + 1]);
-            qs = (qs >> qshift) & uint8_t(0xF);
-
-            const float d = float(data_a[ib].d);
-            const vec2 v = d * float(int(sl | (sh << 4)) - 32) * vec2(kvalues_iq4nl[qs.x], kvalues_iq4nl[qs.y]);
-
-            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
-            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
-#elif defined(DATA_A_IQ4_NL)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + 2 * loadr_a;
-
-            const uint ib = idx / 8;
-            const uint iqs = idx & 0x07;
-
-            const FLOAT_TYPE d = FLOAT_TYPE(data_a_packed16[ib].d);
-            const uint vui = uint(data_a_packed16[ib].qs[iqs]);
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(kvalues_iq4nl[vui & 0xF]) * d;
-            buf_a[buf_idx + 1 ] = FLOAT_TYPE(kvalues_iq4nl[bitfieldExtract(vui, 8, 4)]) * d;
-            buf_a[buf_idx + 16] = FLOAT_TYPE(kvalues_iq4nl[bitfieldExtract(vui, 4, 4)]) * d;
-            buf_a[buf_idx + 17] = FLOAT_TYPE(kvalues_iq4nl[vui >> 12]) * d;
-#elif defined(DATA_A_MXFP4)
-            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
-            const uint buf_idx = (loadc_a + l) * SHMEM_STRIDE + 2 * loadr_a;
-
-            const uint ib = idx / 8;
-            const uint iqs = (idx & 0x07) * 2;
-
-            const float d = e8m0_to_fp32(data_a[ib].e);
-            const uint vui = uint(data_a[ib].qs[iqs]);
-            const uint vui2 = uint(data_a[ib].qs[iqs+1]);
-
-            buf_a[buf_idx     ] = FLOAT_TYPE(kvalues_mxfp4[vui & 0xF] * d);
-            buf_a[buf_idx + 16] = FLOAT_TYPE(kvalues_mxfp4[vui >>  4] * d);
-            buf_a[buf_idx +  1] = FLOAT_TYPE(kvalues_mxfp4[vui2 & 0xF] * d);
-            buf_a[buf_idx + 17] = FLOAT_TYPE(kvalues_mxfp4[vui2 >>  4] * d);
-#endif
+            load_a_to_shmem(pos_a, loadr_a, loadc_a + l, ir * BM + loadc_a + l, block + loadr_a, end_k);
         }
         [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {
-#if LOAD_VEC_B == 8
-#ifdef MUL_MAT_ID
-            const u16vec2 row_idx = row_ids[loadc_b + l];
-            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b;
+#if !defined(MUL_MAT_ID)
+            load_b_to_shmem(pos_b, loadr_b, loadc_b + l, ic * BN + loadc_b + l, block + loadr_b, end_k);
 #else
-            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
-#endif
-            const uint buf_idx = (loadc_b + l) * SHMEM_STRIDE + loadr_b * LOAD_VEC_B;
-#if defined(DATA_B_BF16)
-            B_TYPE32 bb = TO_FLOAT_TYPE(data_b[idx]);
-#else
-            B_TYPE32 bb = B_TYPE32(data_b[idx]);
-#endif
-            buf_b[buf_idx + 0] = FLOAT_TYPE(bb[0].x);
-            buf_b[buf_idx + 1] = FLOAT_TYPE(bb[0].y);
-            buf_b[buf_idx + 2] = FLOAT_TYPE(bb[0].z);
-            buf_b[buf_idx + 3] = FLOAT_TYPE(bb[0].w);
-            buf_b[buf_idx + 4] = FLOAT_TYPE(bb[1].x);
-            buf_b[buf_idx + 5] = FLOAT_TYPE(bb[1].y);
-            buf_b[buf_idx + 6] = FLOAT_TYPE(bb[1].z);
-            buf_b[buf_idx + 7] = FLOAT_TYPE(bb[1].w);
-#elif LOAD_VEC_B == 4
-#ifdef MUL_MAT_ID
-            const u16vec2 row_idx = row_ids[loadc_b + l];
-            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b;
-#else
-            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
-#endif
-            const uint buf_idx = (loadc_b + l) * SHMEM_STRIDE + loadr_b * LOAD_VEC_B;
-#if defined(DATA_B_BF16)
-            B_TYPE32 bb = TO_FLOAT_TYPE(data_b[idx]);
-#else
-            B_TYPE32 bb = B_TYPE32(data_b[idx]);
-#endif
-            buf_b[buf_idx + 0] = FLOAT_TYPE(bb.x);
-            buf_b[buf_idx + 1] = FLOAT_TYPE(bb.y);
-            buf_b[buf_idx + 2] = FLOAT_TYPE(bb.z);
-            buf_b[buf_idx + 3] = FLOAT_TYPE(bb.w);
-#elif !MUL_MAT_ID
-            if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) {
-                buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = TO_FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]);
-            } else {
-                buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = FLOAT_TYPE(0.0f);
-            }
-#else
-            const uint row_i = ic * BN + loadc_b + l;
-            if (row_i < _ne1 && block + loadr_b < end_k) {
-                const u16vec2 row_idx = row_ids[loadc_b + l];
-                buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = TO_FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + loadr_b]);
-            } else {
-                buf_b[(loadc_b + l) * SHMEM_STRIDE + loadr_b] = FLOAT_TYPE(0.0f);
-            }
+            load_b_to_shmem(pos_b, loadr_b, loadc_b + l, ic, _ne1, block + loadr_b, end_k);
 #endif
         }
 

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.comp

@@ -0,0 +1,568 @@
+void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uint idx_m, const uint idx_k, const uint end_k) {
+#if defined(DATA_A_F32) || defined(DATA_A_F16)
+#if LOAD_VEC_A == 8
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+            FLOAT_TYPE_VEC8 aa = FLOAT_TYPE_VEC8(data_a[idx]);
+            buf_a[buf_idx    ] = aa[0].x;
+            buf_a[buf_idx + 1] = aa[0].y;
+            buf_a[buf_idx + 2] = aa[0].z;
+            buf_a[buf_idx + 3] = aa[0].w;
+            buf_a[buf_idx + 4] = aa[1].x;
+            buf_a[buf_idx + 5] = aa[1].y;
+            buf_a[buf_idx + 6] = aa[1].z;
+            buf_a[buf_idx + 7] = aa[1].w;
+#elif LOAD_VEC_A == 4
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+            FLOAT_TYPE_VEC4 aa = FLOAT_TYPE_VEC4(data_a[idx]);
+            buf_a[buf_idx    ] = aa.x;
+            buf_a[buf_idx + 1] = aa.y;
+            buf_a[buf_idx + 2] = aa.z;
+            buf_a[buf_idx + 3] = aa.w;
+#else
+            if (idx_m < p.M && idx_k < end_k) {
+                buf_a[col * SHMEM_STRIDE + row] = FLOAT_TYPE(data_a[pos_a + col * p.stride_a + row]);
+            } else {
+                buf_a[col * SHMEM_STRIDE + row] = FLOAT_TYPE(0.0f);
+            }
+#endif
+#elif defined(DATA_A_BF16)
+#if LOAD_VEC_A == 4
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+            FLOAT_TYPE_VEC4 aa = FLOAT_TYPE_VEC4(TO_FLOAT_TYPE(data_a[idx]));
+            buf_a[buf_idx    ] = aa.x;
+            buf_a[buf_idx + 1] = aa.y;
+            buf_a[buf_idx + 2] = aa.z;
+            buf_a[buf_idx + 3] = aa.w;
+#else
+            if (idx_m < p.M && idx_k < end_k) {
+                buf_a[col * SHMEM_STRIDE + row] = TO_FLOAT_TYPE(data_a[pos_a + col * p.stride_a + row]);
+            } else {
+                buf_a[col * SHMEM_STRIDE + row] = TO_FLOAT_TYPE(uint16_t(0));
+            }
+#endif
+#elif defined(DATA_A_Q4_0)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + 4 * row;
+
+            const uint ib = idx / 4;
+            const uint iqs = idx & 0x03;
+
+            const float d = float(data_a_packed16[ib].d);
+            const uint vui = uint(data_a_packed16[ib].qs[2*iqs]) | (uint(data_a_packed16[ib].qs[2*iqs + 1]) << 16);
+            const vec4 v0 = (vec4(unpack8(vui & 0x0F0F0F0F)) - 8.0f) * d;
+            const vec4 v1 = (vec4(unpack8((vui >> 4) & 0x0F0F0F0F)) - 8.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v0.x);
+            buf_a[buf_idx + 1 ] = FLOAT_TYPE(v0.y);
+            buf_a[buf_idx + 2 ] = FLOAT_TYPE(v0.z);
+            buf_a[buf_idx + 3 ] = FLOAT_TYPE(v0.w);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v1.x);
+            buf_a[buf_idx + 17] = FLOAT_TYPE(v1.y);
+            buf_a[buf_idx + 18] = FLOAT_TYPE(v1.z);
+            buf_a[buf_idx + 19] = FLOAT_TYPE(v1.w);
+#elif defined(DATA_A_Q4_1)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + 4 * row;
+
+            const uint ib = idx / 4;
+            const uint iqs = idx & 0x03;
+
+            const float d = float(data_a_packed16[ib].d);
+            const float m = float(data_a_packed16[ib].m);
+            const uint vui = uint(data_a_packed16[ib].qs[2*iqs]) | (uint(data_a_packed16[ib].qs[2*iqs + 1]) << 16);
+            const vec4 v0 = vec4(unpack8(vui & 0x0F0F0F0F)) * d + m;
+            const vec4 v1 = vec4(unpack8((vui >> 4) & 0x0F0F0F0F)) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v0.x);
+            buf_a[buf_idx + 1 ] = FLOAT_TYPE(v0.y);
+            buf_a[buf_idx + 2 ] = FLOAT_TYPE(v0.z);
+            buf_a[buf_idx + 3 ] = FLOAT_TYPE(v0.w);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v1.x);
+            buf_a[buf_idx + 17] = FLOAT_TYPE(v1.y);
+            buf_a[buf_idx + 18] = FLOAT_TYPE(v1.z);
+            buf_a[buf_idx + 19] = FLOAT_TYPE(v1.w);
+#elif defined(DATA_A_Q5_0)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + 2 * row;
+
+            const uint ib = idx / 8;
+            const uint iqs = idx & 0x07;
+
+            const float d = float(data_a_packed16[ib].d);
+            const uint uint_qh = uint(data_a_packed16[ib].qh[1]) << 16 | uint(data_a_packed16[ib].qh[0]);
+            const ivec2 qh0 = ivec2(((uint_qh >> 2*iqs) << 4) & 0x10, (uint_qh >> (2*iqs + 12)) & 0x10);
+            const ivec2 qh1 = ivec2(((uint_qh >> (2*iqs + 1)) << 4) & 0x10, (uint_qh >> (2*iqs + 13)) & 0x10);
+
+            const uint vui = uint(data_a_packed16[ib].qs[iqs]);
+            const vec4 v = (vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y) - 16.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1 ] = FLOAT_TYPE(v.z);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+            buf_a[buf_idx + 17] = FLOAT_TYPE(v.w);
+#elif defined(DATA_A_Q5_1)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + 2 * row;
+
+            const uint ib = idx / 8;
+            const uint iqs = idx & 0x07;
+
+            const float d = float(data_a_packed16[ib].d);
+            const float m = float(data_a_packed16[ib].m);
+            const uint uint_qh = data_a_packed16[ib].qh;
+            const ivec2 qh0 = ivec2(((uint_qh >> 2*iqs) << 4) & 0x10, (uint_qh >> (2*iqs + 12)) & 0x10);
+            const ivec2 qh1 = ivec2(((uint_qh >> (2*iqs + 1)) << 4) & 0x10, (uint_qh >> (2*iqs + 13)) & 0x10);
+
+            const uint vui = uint(data_a_packed16[ib].qs[iqs]);
+            const vec4 v = vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1 ] = FLOAT_TYPE(v.z);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+            buf_a[buf_idx + 17] = FLOAT_TYPE(v.w);
+#elif defined(DATA_A_Q8_0)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 8;
+            const uint iqs = idx & 0x07;
+
+            const float d = float(data_a_packed16[ib].d);
+            const i8vec2 v0 = unpack8(int32_t(data_a_packed16[ib].qs[2*iqs])).xy; // vec4 used due to #12147
+            const i8vec2 v1 = unpack8(int32_t(data_a_packed16[ib].qs[2*iqs + 1])).xy;
+            const vec4 v = vec4(v0.x, v0.y, v1.x, v1.y) * d;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE(v.z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE(v.w);
+#elif defined(DATA_A_Q2_K)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                         // 2 values per idx
+            const uint iqs = idx % 128;                        // 0..127
+
+            const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30
+            const uint scalesi = iqs / 8;                      // 0..15
+            const uint qsshift = ((iqs % 64) / 16) * 2;        // 0,2,4,6
+
+            const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]);
+            const uint scales = data_a[ib].scales[scalesi];
+            const vec2 d = vec2(data_a[ib].d);
+
+            const vec2 v = d.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - d.y * float(scales >> 4);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q3_K)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                   // 2 values per idx
+            const uint iqs = idx % 128;                  // 0..127
+
+            const uint n = iqs / 64;                     // 0,1
+            const uint qsi = n * 32 + (iqs % 16) * 2;    // 0,2,4..62
+            const uint hmi =          (iqs % 16) * 2;    // 0,2,4..30
+            const uint j = (iqs % 64) / 4;               // 0..3
+            const uint is = iqs / 8;                     // 0..15
+            const uint halfsplit = ((iqs % 64) / 16);    // 0,1,2,3
+            const uint qsshift = halfsplit * 2;          // 0,2,4,6
+            const uint m = 1 << (4 * n + halfsplit);     // 1,2,4,8,16,32,64,128
+
+            const int8_t us = int8_t(((data_a[ib].scales[is % 8] >> (4 * int(is / 8))) & 0xF)
+                                  | (((data_a[ib].scales[8 + (is % 4)] >> (2 * int(is / 4))) & 3) << 4));
+            const float dl = float(data_a[ib].d) * float(us - 32);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi    ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi    ] & m) != 0) ? 0 : 4)));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[ib].hmask[hmi + 1] & m) != 0) ? 0 : 4)));
+#elif defined(DATA_A_Q4_K)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            const uint scidx0 = (is < 4) ? is : (is + 4);
+            const uint scidx1 = (is < 4) ? is : (is - 4);
+            const uint scidxmask1 = (is < 4) ? 0x30 : 0xC0;
+            const uint scidxshift1 = (is < 4) ? 0 : 2;
+            const uint mbidx0 = is + 4;
+            const uint mbidx1 = (is < 4) ? is + 4 : is;
+            const uint mbidxmask0 = (is < 4) ? 0xF : 0xF0;
+            const uint mbidxshift0 = (is < 4) ? 0 : 4;
+            const uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
+            const uint mbidxshift1 = (is < 4) ? 0 : 2;
+
+            const uint8_t sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
+            const uint8_t mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
+
+            const float d = loadd.x * sc;
+            const float m = -loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF), m));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF), m));
+#elif defined(DATA_A_Q5_K)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+            const uint qhi = (iqs % 16) * 2;           // 0,2,4..30
+
+            const uint8_t hm = uint8_t(1 << (iqs / 16));
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            const uint scidx0 = (is < 4) ? is : (is + 4);
+            const uint scidx1 = (is < 4) ? is : (is - 4);
+            const uint scidxmask1 = (is < 4) ? 0x30 : 0xC0;
+            const uint scidxshift1 = (is < 4) ? 0 : 2;
+            const uint mbidx0 = is + 4;
+            const uint mbidx1 = (is < 4) ? is + 4 : is;
+            const uint mbidxmask0 = (is < 4) ? 0xF : 0xF0;
+            const uint mbidxshift0 = (is < 4) ? 0 : 4;
+            const uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0;
+            const uint mbidxshift1 = (is < 4) ? 0 : 2;
+
+            const uint8_t sc    = uint8_t((data_a[ib].scales[scidx0] & 0xF)                         | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1));
+            const uint8_t mbyte = uint8_t(((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1));
+
+            const float d = loadd.x * sc;
+            const float m = -loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi    ] & hm) != 0 ? 16 : 0), m));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(fma(d, float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0), m));
+#elif defined(DATA_A_Q6_K)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint iqs = idx % 128;                 // 0..127
+
+            const uint n = iqs / 64;                    // 0,1
+            const uint b = (iqs % 64) / 32;             // 0,1
+            const uint is_b = (iqs % 16) / 8;           // 0,1
+            const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
+            const uint is = 8 * n + qhshift + is_b;     // 0..15
+            const uint qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
+            const uint qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
+
+            const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));
+#elif defined(DATA_A_IQ1_S)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 32;                  // 8 values per idx
+            const uint ib32 = (idx % 32) / 4;         // 0..7
+            const uint ib8 = idx % 32;
+
+            const float d = float(data_a[ib].d);
+            const uint qh = data_a[ib].qh[ib32];
+            const uint qs = data_a[ib].qs[ib8];
+            const float dl = d * (2 * bitfieldExtract(qh, 12, 3) + 1);
+            const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
+            const int16_t grid = int16_t(iq1s_grid[qs | (bitfieldExtract(qh, 3 * int(ib8 & 3), 3) << 8)]);
+
+            [[unroll]] for (int k = 0; k < 8; ++k) {
+                buf_a[buf_idx + k] = FLOAT_TYPE(dl * (bitfieldExtract(grid, 2 * k, 2) + delta));
+            }
+#elif defined(DATA_A_IQ1_M)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 32;  // 8 values per idx
+            const uint ib8 = idx % 32;
+            const uint ib16 = ib8 / 2;
+
+            const uint16_t[4] scales = data_a[ib].scales;
+            const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12;
+            const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x);
+            const uint sc = scales[ib8 / 8];
+            const uint qs = data_a[ib].qs[ib8];
+            const uint qh = data_a[ib].qh[ib16] >> (4 * (ib8 & 1));
+            const float dl = d * (2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1);
+            const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA;
+            const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]);
+
+            [[unroll]] for (int k = 0; k < 8; ++k) {
+                buf_a[buf_idx + k] = FLOAT_TYPE(dl * (bitfieldExtract(grid, 2 * k, 2) + delta));
+            }
+#elif defined(DATA_A_IQ2_XXS)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 32;                 // 8 values per idx
+            const uint ib32 = (idx % 32) / 4;         // 0..7
+            const uint ib8 = idx % 4;
+
+            const float d = float(data_a[ib].d);
+            const uint qs = data_a[ib].qs[8 * ib32 + ib8];
+            const uint signs = pack32(u8vec4(
+                data_a[ib].qs[8*ib32 + 4],
+                data_a[ib].qs[8*ib32 + 5],
+                data_a[ib].qs[8*ib32 + 6],
+                data_a[ib].qs[8*ib32 + 7]
+            ));
+            const FLOAT_TYPE db = FLOAT_TYPE(d * 0.25 * (0.5 + (signs >> 28)));
+            const uint32_t sign7 = bitfieldExtract(signs, 7 * int(ib8), 7);
+            const uint sign = sign7 | (bitCount(sign7) << 7);
+            const uvec2 grid = iq2xxs_grid[qs];
+            const vec4 grid0 = vec4(unpack8(grid.x));
+            const vec4 grid1 = vec4(unpack8(grid.y));
+
+            buf_a[buf_idx    ] = db * FLOAT_TYPE((sign &   1) != 0 ? -grid0.x : grid0.x);
+            buf_a[buf_idx + 1] = db * FLOAT_TYPE((sign &   2) != 0 ? -grid0.y : grid0.y);
+            buf_a[buf_idx + 2] = db * FLOAT_TYPE((sign &   4) != 0 ? -grid0.z : grid0.z);
+            buf_a[buf_idx + 3] = db * FLOAT_TYPE((sign &   8) != 0 ? -grid0.w : grid0.w);
+            buf_a[buf_idx + 4] = db * FLOAT_TYPE((sign &  16) != 0 ? -grid1.x : grid1.x);
+            buf_a[buf_idx + 5] = db * FLOAT_TYPE((sign &  32) != 0 ? -grid1.y : grid1.y);
+            buf_a[buf_idx + 6] = db * FLOAT_TYPE((sign &  64) != 0 ? -grid1.z : grid1.z);
+            buf_a[buf_idx + 7] = db * FLOAT_TYPE((sign & 128) != 0 ? -grid1.w : grid1.w);
+#elif defined(DATA_A_IQ2_XS)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 32;            // 8 values per idx
+            const uint ib32 = (idx % 32) / 4;    // 0..7
+            const uint ib8 = idx % 4;            // 0..3
+
+            const float d = float(data_a[ib].d);
+            const uint scale = (data_a[ib].scales[ib32] >> (2 * (ib8 & 2))) & 0xf;
+            const FLOAT_TYPE db = FLOAT_TYPE(d * 0.25 * (0.5 + scale));
+            const uint qs = data_a[ib].qs[4 * ib32 + ib8];
+            const uint sign7 = qs >> 9;
+            const uint sign = sign7 | (bitCount(sign7) << 7);
+            const uvec2 grid = iq2xs_grid[qs & 511];
+            const vec4 grid0 = vec4(unpack8(grid.x));
+            const vec4 grid1 = vec4(unpack8(grid.y));
+
+            buf_a[buf_idx    ] = db * FLOAT_TYPE((sign &   1) != 0 ? -grid0.x : grid0.x);
+            buf_a[buf_idx + 1] = db * FLOAT_TYPE((sign &   2) != 0 ? -grid0.y : grid0.y);
+            buf_a[buf_idx + 2] = db * FLOAT_TYPE((sign &   4) != 0 ? -grid0.z : grid0.z);
+            buf_a[buf_idx + 3] = db * FLOAT_TYPE((sign &   8) != 0 ? -grid0.w : grid0.w);
+            buf_a[buf_idx + 4] = db * FLOAT_TYPE((sign &  16) != 0 ? -grid1.x : grid1.x);
+            buf_a[buf_idx + 5] = db * FLOAT_TYPE((sign &  32) != 0 ? -grid1.y : grid1.y);
+            buf_a[buf_idx + 6] = db * FLOAT_TYPE((sign &  64) != 0 ? -grid1.z : grid1.z);
+            buf_a[buf_idx + 7] = db * FLOAT_TYPE((sign & 128) != 0 ? -grid1.w : grid1.w);
+#elif defined(DATA_A_IQ2_S)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 32;  // 8 values per idx
+            const uint ib8 = idx % 32; // 0..31
+            const uint ib32 = ib8 / 4; // 0..7
+
+            const uint scale = (data_a[ib].scales[ib32] >> (2 * (ib8 & 2))) & 0xf;
+            const uint qs = data_a[ib].qs[ib8];
+            const uint qh = data_a[ib].qh[ib32];
+            const uint qhshift = 2 * (ib8 % 4);
+            const uint sign = data_a[ib].qs[QUANT_K / 8 + ib8];
+
+            const float d = float(data_a[ib].d);
+            const FLOAT_TYPE db = FLOAT_TYPE(d * 0.25 * (0.5 + scale));
+            const uvec2 grid = iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)];
+            const vec4 grid0 = vec4(unpack8(grid.x));
+            const vec4 grid1 = vec4(unpack8(grid.y));
+
+            buf_a[buf_idx    ] = db * FLOAT_TYPE((sign &   1) != 0 ? -grid0.x : grid0.x);
+            buf_a[buf_idx + 1] = db * FLOAT_TYPE((sign &   2) != 0 ? -grid0.y : grid0.y);
+            buf_a[buf_idx + 2] = db * FLOAT_TYPE((sign &   4) != 0 ? -grid0.z : grid0.z);
+            buf_a[buf_idx + 3] = db * FLOAT_TYPE((sign &   8) != 0 ? -grid0.w : grid0.w);
+            buf_a[buf_idx + 4] = db * FLOAT_TYPE((sign &  16) != 0 ? -grid1.x : grid1.x);
+            buf_a[buf_idx + 5] = db * FLOAT_TYPE((sign &  32) != 0 ? -grid1.y : grid1.y);
+            buf_a[buf_idx + 6] = db * FLOAT_TYPE((sign &  64) != 0 ? -grid1.z : grid1.z);
+            buf_a[buf_idx + 7] = db * FLOAT_TYPE((sign & 128) != 0 ? -grid1.w : grid1.w);
+#elif defined(DATA_A_IQ3_XXS)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 64;            // 4 values per idx
+            const uint iqs = idx % 64;           // 0..63
+            const uint is = QUANT_K / 4 + 4 * (iqs / 8); // 8 values
+
+            const float d = float(data_a[ib].d);
+            const uint qs = data_a[ib].qs[iqs];
+            const uint signs = pack32(u8vec4(
+                data_a[ib].qs[is+0],
+                data_a[ib].qs[is+1],
+                data_a[ib].qs[is+2],
+                data_a[ib].qs[is+3]
+            ));
+            const float db = d * 0.5 * (0.5 + (signs >> 28));
+            const uint32_t sign7 = bitfieldExtract(signs, 7 * (int(iqs / 2) % 4), 7);
+            const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (4 * (idx % 2));
+            const uint grid = iq3xxs_grid[qs];
+            const vec4 v = db * vec4(unpack8(grid));
+
+            buf_a[buf_idx    ] = FLOAT_TYPE((sign &   1) != 0 ? -v.x : v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE((sign &   2) != 0 ? -v.y : v.y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE((sign &   4) != 0 ? -v.z : v.z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE((sign &   8) != 0 ? -v.w : v.w);
+#elif defined(DATA_A_IQ3_S)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 64;            // 4 values per idx
+            const uint iqs = idx % 64;           // 0..63
+            const uint iqh = iqs / 8;
+
+            const float d = float(data_a[ib].d);
+            const uint qs = data_a[ib].qs[iqs];
+            const uint qh = data_a[ib].qh[iqh];
+            const int8_t sign = int8_t(data_a[ib].signs[iqs / 2] >> (4 * (idx % 2)));
+            const uint scale = data_a[ib].scales[iqs / 16];
+            const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(sign << 1, sign)));
+            const float db = d * (1 + 2 * ((scale >> (4 * (iqh & 1))) & 0xf));
+            const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)];
+            const vec4 v = db * vec4(unpack8(grid));
+
+            buf_a[buf_idx    ] = FLOAT_TYPE((sign &   1) != 0 ? -v.x : v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE((sign &   2) != 0 ? -v.y : v.y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE((sign &   4) != 0 ? -v.z : v.z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE((sign &   8) != 0 ? -v.w : v.w);
+#elif defined(DATA_A_IQ4_XS)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint ib32 = (idx % 128) / 16;         // 0..7
+            const uint iq = 16 * ib32 + 2 * (idx % 8);
+
+            const uint sl = (data_a[ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF;
+            const uint sh = ((data_a[ib].scales_h) >> (2 * ib32)) & 3;
+            const uint qshift = (idx & 8) >> 1;
+            u8vec2 qs = u8vec2(data_a[ib].qs[iq], data_a[ib].qs[iq + 1]);
+            qs = (qs >> qshift) & uint8_t(0xF);
+
+            const float d = float(data_a[ib].d);
+            const vec2 v = d * float(int(sl | (sh << 4)) - 32) * vec2(kvalues_iq4nl[qs.x], kvalues_iq4nl[qs.y]);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_IQ4_NL)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + 2 * row;
+
+            const uint ib = idx / 8;
+            const uint iqs = idx & 0x07;
+
+            const FLOAT_TYPE d = FLOAT_TYPE(data_a_packed16[ib].d);
+            const uint vui = uint(data_a_packed16[ib].qs[iqs]);
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(kvalues_iq4nl[vui & 0xF]) * d;
+            buf_a[buf_idx + 1 ] = FLOAT_TYPE(kvalues_iq4nl[bitfieldExtract(vui, 8, 4)]) * d;
+            buf_a[buf_idx + 16] = FLOAT_TYPE(kvalues_iq4nl[bitfieldExtract(vui, 4, 4)]) * d;
+            buf_a[buf_idx + 17] = FLOAT_TYPE(kvalues_iq4nl[vui >> 12]) * d;
+#elif defined(DATA_A_MXFP4)
+            const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
+            const uint buf_idx = col * SHMEM_STRIDE + 2 * row;
+
+            const uint ib = idx / 8;
+            const uint iqs = (idx & 0x07) * 2;
+
+            const float d = e8m0_to_fp32(data_a[ib].e);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const uint vui2 = uint(data_a[ib].qs[iqs+1]);
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(kvalues_mxfp4[vui & 0xF] * d);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(kvalues_mxfp4[vui >>  4] * d);
+            buf_a[buf_idx +  1] = FLOAT_TYPE(kvalues_mxfp4[vui2 & 0xF] * d);
+            buf_a[buf_idx + 17] = FLOAT_TYPE(kvalues_mxfp4[vui2 >>  4] * d);
+#endif
+}
+
+#if !defined(MUL_MAT_ID)
+void load_b_to_shmem(const uint pos_b, const uint row, const uint col, const uint idx_n, const uint idx_k, const uint end_k) {
+#if LOAD_VEC_B == 8
+            // Not supported for b_type bf16 because bf16mat2x4 does not exist
+            const uint idx = pos_b + col * p.stride_b / LOAD_VEC_B + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B;
+            FLOAT_TYPE_VEC8 bb = FLOAT_TYPE_VEC8(data_b[idx]);
+            buf_b[buf_idx + 0] = bb[0].x;
+            buf_b[buf_idx + 1] = bb[0].y;
+            buf_b[buf_idx + 2] = bb[0].z;
+            buf_b[buf_idx + 3] = bb[0].w;
+            buf_b[buf_idx + 4] = bb[1].x;
+            buf_b[buf_idx + 5] = bb[1].y;
+            buf_b[buf_idx + 6] = bb[1].z;
+            buf_b[buf_idx + 7] = bb[1].w;
+#elif LOAD_VEC_B == 4
+            const uint idx = pos_b + col * p.stride_b / LOAD_VEC_B + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B;
+#if defined(DATA_B_BF16)
+            FLOAT_TYPE_VEC4 bb = FLOAT_TYPE_VEC4(TO_FLOAT_TYPE(data_b[idx]));
+#else
+            FLOAT_TYPE_VEC4 bb = FLOAT_TYPE_VEC4(data_b[idx]);
+#endif
+            buf_b[buf_idx + 0] = bb.x;
+            buf_b[buf_idx + 1] = bb.y;
+            buf_b[buf_idx + 2] = bb.z;
+            buf_b[buf_idx + 3] = bb.w;
+#else // LOAD_VEC_B == 1
+            if (idx_n < p.N && idx_k < end_k) {
+                buf_b[col * SHMEM_STRIDE + row] = TO_FLOAT_TYPE(data_b[pos_b + col * p.stride_b + row]);
+            } else {
+                buf_b[col * SHMEM_STRIDE + row] = FLOAT_TYPE(0.0f);
+            }
+#endif
+}
+#else
+void load_b_to_shmem(const uint pos_b, const uint row, const uint col, const uint ic, const uint _ne1, const uint idx_k, const uint end_k) {
+#if LOAD_VEC_B == 8
+            // Not supported for b_type bf16 because bf16mat2x4 does not exist
+            const u16vec2 row_idx = row_ids[col];
+            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B;
+            FLOAT_TYPE_VEC8 bb = FLOAT_TYPE_VEC8(data_b[idx]);
+            buf_b[buf_idx + 0] = bb[0].x;
+            buf_b[buf_idx + 1] = bb[0].y;
+            buf_b[buf_idx + 2] = bb[0].z;
+            buf_b[buf_idx + 3] = bb[0].w;
+            buf_b[buf_idx + 4] = bb[1].x;
+            buf_b[buf_idx + 5] = bb[1].y;
+            buf_b[buf_idx + 6] = bb[1].z;
+            buf_b[buf_idx + 7] = bb[1].w;
+#elif LOAD_VEC_B == 4
+            const u16vec2 row_idx = row_ids[col];
+            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + row;
+            const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B;
+#if defined(DATA_B_BF16)
+            FLOAT_TYPE_VEC4 bb = FLOAT_TYPE_VEC4(TO_FLOAT_TYPE(data_b[idx]));
+#else
+            FLOAT_TYPE_VEC4 bb = FLOAT_TYPE_VEC4(data_b[idx]);
+#endif
+            buf_b[buf_idx + 0] = bb.x;
+            buf_b[buf_idx + 1] = bb.y;
+            buf_b[buf_idx + 2] = bb.z;
+            buf_b[buf_idx + 3] = bb.w;
+#else // LOAD_VEC_B == 1
+            const uint row_i = ic * BN + col;
+            if (row_i < _ne1 && idx_k < end_k) {
+                const u16vec2 row_idx = row_ids[col];
+                buf_b[col * SHMEM_STRIDE + row] = TO_FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + row]);
+            } else {
+                buf_b[col * SHMEM_STRIDE + row] = FLOAT_TYPE(0.0f);
+            }
+#endif
+}
+#endif

ggml/src/ggml-vulkan/vulkan-shaders/timestep_embedding.comp

@@ -24,11 +24,12 @@ void main() {
     const uint j = gl_GlobalInvocationID.x;
     const uint d_offset = i * p.nb1;
 
-    if (p.dim % 2 != 0 && j == ((p.dim + 1) / 2)) {
-        data_d[d_offset + p.dim] = 0.f;
+    const uint half_dim = p.dim / 2;
+
+    if (p.dim % 2 != 0 && j == half_dim) {
+        data_d[d_offset + 2 * half_dim] = 0.f;
     }
 
-    const uint half_dim = p.dim / 2;
     if (j >= half_dim) {
         return;
     }

ggml/src/ggml-vulkan/vulkan-shaders/types.comp

@@ -13,13 +13,10 @@
 
 #if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1
 #define A_TYPE float
-#define A_TYPE32 float
 #elif LOAD_VEC_A == 4
 #define A_TYPE vec4
-#define A_TYPE32 vec4
 #elif LOAD_VEC_A == 8
 #define A_TYPE mat2x4
-#define A_TYPE32 mat2x4
 #endif
 #endif
 
@@ -29,13 +26,10 @@
 
 #if !defined(LOAD_VEC_A) || LOAD_VEC_A == 1
 #define A_TYPE float16_t
-#define A_TYPE32 float
 #elif LOAD_VEC_A == 4
 #define A_TYPE f16vec4
-#define A_TYPE32 vec4
 #elif LOAD_VEC_A == 8
 #define A_TYPE f16mat2x4
-#define A_TYPE32 mat2x4
 #endif
 #endif