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llama.cpp
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Merge branch 'master' into imatrix

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Ed Addario 2025-12-22 09:28:25 +00:00
parent c3b6685599 e1f15b454f
commit 9537493150
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175 changed files with 8455 additions and 3063 deletions
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1
.github/ISSUE_TEMPLATE/019-bug-misc.yml vendored
View File

@ -86,6 +86,7 @@ body:
description: >
If applicable, please copy and paste any relevant log output, including any generated text.
This will be automatically formatted into code, so no need for backticks.
If you are encountering problems specifically with the `llama_params_fit` module, always upload `--verbose` logs as well.
render: shell
validations:
required: false

29
.github/workflows/build.yml vendored
View File

@ -70,6 +70,7 @@ jobs:
with:
key: macOS-latest-cmake-arm64
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build
id: cmake_build
@ -106,6 +107,7 @@ jobs:
with:
key: macOS-latest-cmake-x64
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build
id: cmake_build
@ -142,6 +144,7 @@ jobs:
with:
key: macOS-latest-cmake-arm64-webgpu
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dawn Dependency
id: dawn-depends
@ -195,6 +198,7 @@ jobs:
with:
key: ubuntu-cpu-cmake-${{ matrix.build }}
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build Dependencies
id: build_depends
@ -276,6 +280,7 @@ jobs:
with:
key: ubuntu-latest-cmake-sanitizer-${{ matrix.sanitizer }}
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -396,6 +401,7 @@ jobs:
with:
key: ubuntu-24-cmake-vulkan-deb
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -431,6 +437,7 @@ jobs:
with:
key: ubuntu-24-cmake-vulkan
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -490,6 +497,7 @@ jobs:
with:
key: ubuntu-24-cmake-webgpu
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -562,6 +570,7 @@ jobs:
with:
key: ubuntu-latest-wasm-webgpu
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Install Emscripten
run: |
@ -609,6 +618,7 @@ jobs:
with:
key: ubuntu-22-cmake-hip
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build with native CMake HIP support
id: cmake_build
@ -641,6 +651,7 @@ jobs:
with:
key: ubuntu-22-cmake-musa
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build with native CMake MUSA support
id: cmake_build
@ -688,6 +699,7 @@ jobs:
with:
key: ubuntu-22-cmake-sycl
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build
id: cmake_build
@ -738,6 +750,7 @@ jobs:
with:
key: ubuntu-22-cmake-sycl-fp16
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build
id: cmake_build
@ -771,6 +784,7 @@ jobs:
with:
key: macOS-latest-cmake-ios
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build
id: cmake_build
@ -802,6 +816,7 @@ jobs:
with:
key: macOS-latest-cmake-tvos
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build
id: cmake_build
@ -863,6 +878,7 @@ jobs:
with:
key: macOS-latest-swift
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Download xcframework artifact
uses: actions/download-artifact@v4
@ -905,6 +921,7 @@ jobs:
key: windows-msys2
variant: ccache
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Setup ${{ matrix.sys }}
uses: msys2/setup-msys2@v2
@ -973,6 +990,7 @@ jobs:
key: windows-latest-cmake-${{ matrix.build }}
variant: ccache
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Download OpenBLAS
id: get_openblas
@ -1077,6 +1095,7 @@ jobs:
with:
key: ubuntu-latest-cmake-cuda
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build with CMake
run: |
@ -1109,6 +1128,7 @@ jobs:
key: windows-cuda-${{ matrix.cuda }}
variant: ccache
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Install Cuda Toolkit
uses: ./.github/actions/windows-setup-cuda
@ -1160,6 +1180,7 @@ jobs:
key: windows-latest-cmake-sycl
variant: ccache
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Install
run: |
@ -1221,6 +1242,7 @@ jobs:
with:
key: ${{ github.job }}
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Build
id: cmake_build
@ -1466,6 +1488,7 @@ jobs:
with:
key: ggml-ci-x64-cpu-low-perf
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -1491,6 +1514,7 @@ jobs:
with:
key: ggml-ci-arm64-cpu-low-perf
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -1516,6 +1540,7 @@ jobs:
with:
key: ggml-ci-x64-cpu-high-perf
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -1541,6 +1566,7 @@ jobs:
with:
key: ggml-ci-arm64-cpu-high-perf
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -1566,6 +1592,7 @@ jobs:
with:
key: ggml-ci-arm64-cpu-high-perf-sve
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -1701,6 +1728,7 @@ jobs:
with:
key: ggml-ci-arm64-cpu-kleidiai
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Dependencies
id: depends
@ -2084,6 +2112,7 @@ jobs:
with:
key: ggml-ci-arm64-graviton4-kleidiai
evict-old-files: 1d
save: ${{ github.event_name == 'push' && github.ref == 'refs/heads/master' }}
- name: Test
id: ggml-ci

54
.github/workflows/release.yml vendored
View File

@ -66,16 +66,9 @@ jobs:
id: pack_artifacts
run: |
cp LICENSE ./build/bin/
zip -y -r llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip ./build/bin/*
tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz -s ",./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
- name: Upload artifacts (zip)
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.zip
name: llama-bin-macos-arm64.zip
- name: Upload artifacts (tar)
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-macos-arm64.tar.gz
@ -127,16 +120,9 @@ jobs:
id: pack_artifacts
run: |
cp LICENSE ./build/bin/
zip -y -r llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip ./build/bin/*
tar -czvf llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz -s ",./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
- name: Upload artifacts (zip)
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.zip
name: llama-bin-macos-x64.zip
- name: Upload artifacts (tar)
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-macos-x64.tar.gz
@ -196,16 +182,9 @@ jobs:
id: pack_artifacts
run: |
cp LICENSE ./build/bin/
zip -y -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip ./build/bin/*
tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
- name: Upload artifacts (zip)
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.zip
name: llama-bin-ubuntu-${{ matrix.build }}.zip
- name: Upload artifacts (tar)
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-${{ matrix.build }}.tar.gz
@ -256,16 +235,9 @@ jobs:
id: pack_artifacts
run: |
cp LICENSE ./build/bin/
zip -y -r llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip ./build/bin/*
tar -czvf llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
- name: Upload artifacts (zip)
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.zip
name: llama-bin-ubuntu-vulkan-x64.zip
- name: Upload artifacts (tar)
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-vulkan-x64.tar.gz
@ -716,16 +688,9 @@ jobs:
- name: Pack artifacts
id: pack_artifacts
run: |
zip -y -r llama-${{ steps.tag.outputs.name }}-xcframework.zip build-apple/llama.xcframework
tar -czvf llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz -C build-apple llama.xcframework
- name: Upload artifacts (zip)
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-xcframework.zip
name: llama-${{ steps.tag.outputs.name }}-xcframework.zip
- name: Upload artifacts (tar)
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz
@ -797,7 +762,7 @@ jobs:
cp LICENSE ./build/bin/
tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
- name: Upload artifacts (tar)
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz
@ -889,9 +854,6 @@ jobs:
with:
tag_name: ${{ steps.tag.outputs.name }}
body: |
> [!WARNING]
> **Release Format Update**: Linux releases will soon use .tar.gz archives instead of .zip. Please make the necessary changes to your deployment scripts.
<details open>
${{ github.event.head_commit.message }}
@ -911,8 +873,8 @@ jobs:
**Windows:**
- [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-x64.zip)
- [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-arm64.zip)
- [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip)
- [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.1-x64.zip)
- [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip) - [CUDA 12.4 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-12.4-x64.zip)
- [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.1-x64.zip) - [CUDA 13.1 DLLs](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/cudart-llama-bin-win-cuda-13.1-x64.zip)
- [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
- [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
- [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip)

106
.github/workflows/server-webui.yml vendored
View File

@ -31,9 +31,10 @@ concurrency:
cancel-in-progress: true
jobs:
webui-setup:
name: WebUI Setup
webui-check:
name: WebUI Checks
runs-on: ubuntu-latest
continue-on-error: true
steps:
- name: Checkout code
uses: actions/checkout@v4
@ -42,137 +43,66 @@ jobs:
ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
- name: Setup Node.js
id: node
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'
id: setup
if: ${{ steps.node.conclusion == 'success' }}
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
if: ${{ always() && steps.setup.conclusion == 'success' }}
run: npm run check
working-directory: tools/server/webui
- name: Run linting
if: ${{ always() && steps.setup.conclusion == 'success' }}
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
if: ${{ always() && steps.setup.conclusion == 'success' }}
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
id: playwright
if: ${{ always() && steps.setup.conclusion == 'success' }}
run: npx playwright install --with-deps
working-directory: tools/server/webui
- name: Build Storybook
if: ${{ always() && steps.playwright.conclusion == 'success' }}
run: npm run build-storybook
working-directory: tools/server/webui
- name: Run Client tests
if: ${{ always() && steps.playwright.conclusion == 'success' }}
run: npm run test:client
working-directory: tools/server/webui
- name: Run Server tests
run: npm run test:server
- name: Run Unit tests
if: ${{ always() && steps.playwright.conclusion == 'success' }}
run: npm run test:unit
working-directory: tools/server/webui
- name: Run UI tests
if: ${{ always() && steps.playwright.conclusion == 'success' }}
run: npm run test:ui -- --testTimeout=60000
working-directory: tools/server/webui
- name: Run E2E tests
if: ${{ always() && steps.playwright.conclusion == 'success' }}
run: npm run test:e2e
working-directory: tools/server/webui
server-build:
needs: [webui-tests]
runs-on: ubuntu-latest
strategy:

21
.github/copilot-instructions.md → AGENTS.md
View File

@ -1,4 +1,4 @@
# Copilot Instructions for llama.cpp
# Instructions for llama.cpp
## Repository Overview
@ -12,6 +12,24 @@ llama.cpp is a large-scale C/C++ project for efficient LLM (Large Language Model
- **Backends supported**: CPU (AVX/NEON/RVV optimized), CUDA, Metal, Vulkan, SYCL, ROCm, MUSA
- **License**: MIT
## Disclose AI Usage
It is crucial to remind contributors that the project mandates disclosure of any AI usage in pull requests. This requirement stems from the potential for AI-generated code to include suboptimal optimizations and hidden bugs, owing to the inherent overconfidence in AI outputs.
When generating significant portions of code, address this by:
- Informing the user that AI-generated content may be rejected by maintainers.
- Clearly marking AI-generated code in commit messages and comments.
- Example of commit message: `[AI] Fix a race condition in ...`
- Example of code comment: `// [AI] spawn a new thread ...`
These measures apply to:
- Changes resulting in large portions of code or complex logic.
- Modifications or additions to public APIs in `llama.h`, `ggml.h`, or `mtmd.h`.
- Backend-related changes, such as those involving CPU, CUDA, Metal, Vulkan, etc.
- Modifications to `tools/server`.
Note: These measures can be omitted for small fixes or trivial changes.
## Build Instructions
### Prerequisites
@ -251,6 +269,7 @@ Primary tools:
- **Cross-platform compatibility**: Test on Linux, macOS, Windows when possible
- **Performance focus**: This is a performance-critical inference library
- **API stability**: Changes to `include/llama.h` require careful consideration
- **Disclose AI Usage**: Refer to the "Disclose AI Usage" earlier in this document
### Git Workflow
- Always create feature branches from `master`

2
CODEOWNERS
View File

@ -32,7 +32,7 @@
/examples/export-docs/ @ggerganov
/examples/gen-docs/ @ggerganov
/examples/gguf/ @ggerganov
/examples/llama.android/ @ggerganov
/examples/llama.android/ @ggerganov @hanyin-arm @naco-siren
/examples/llama.swiftui/ @ggerganov
/examples/llama.vim @ggerganov
/examples/lookahead/ @ggerganov

1
README.md
View File

@ -190,6 +190,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama)
- Delphi [Embarcadero/llama-cpp-delphi](https://github.com/Embarcadero/llama-cpp-delphi)
- Go (no CGo needed): [hybridgroup/yzma](https://github.com/hybridgroup/yzma)
- Android: [llama.android](/examples/llama.android)
</details>

7
common/CMakeLists.txt
View File

@ -85,6 +85,9 @@ add_library(${TARGET} STATIC
unicode.h
)
target_include_directories(${TARGET} PUBLIC . ../vendor)
target_compile_features (${TARGET} PUBLIC cxx_std_17)
if (BUILD_SHARED_LIBS)
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
endif()
@ -151,9 +154,7 @@ if (LLAMA_LLGUIDANCE)
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance ${LLGUIDANCE_PLATFORM_LIBS})
endif ()
target_include_directories(${TARGET} PUBLIC . ../vendor)
target_compile_features (${TARGET} PUBLIC cxx_std_17)
target_link_libraries (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
#

203
common/arg.cpp
View File

@ -96,6 +96,11 @@ common_arg & common_arg::set_sparam() {
return *this;
}
common_arg & common_arg::set_preset_only() {
is_preset_only = true;
return *this;
}
bool common_arg::in_example(enum llama_example ex) {
return examples.find(ex) != examples.end();
}
@ -420,6 +425,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
}
};
std::set<std::string> seen_args;
for (int i = 1; i < argc; i++) {
const std::string arg_prefix = "--";
@ -430,6 +437,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
if (arg_to_options.find(arg) == arg_to_options.end()) {
throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
}
if (!seen_args.insert(arg).second) {
LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
}
auto & tmp = arg_to_options[arg];
auto opt = *tmp.first;
bool is_positive = tmp.second;
@ -750,6 +760,8 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
}
};
std::set<std::string> seen_args;
for (int i = 1; i < argc; i++) {
const std::string arg_prefix = "--";
@ -760,8 +772,16 @@ bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<com
if (arg_to_options.find(arg) == arg_to_options.end()) {
throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
}
if (!seen_args.insert(arg).second) {
LOG_WRN("DEPRECATED: argument '%s' specified multiple times, use comma-separated values instead (only last value will be used)\n", arg.c_str());
}
auto opt = *arg_to_options[arg];
std::string val;
if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) {
// bool arg (need to reverse the meaning for negative args)
bool is_neg = std::find(opt.args_neg.begin(), opt.args_neg.end(), arg) != opt.args_neg.end();
val = is_neg ? "0" : "1";
}
if (opt.value_hint != nullptr) {
// arg with single value
check_arg(i);
@ -863,7 +883,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
sampler_type_chars += common_sampler_type_to_chr(sampler);
sampler_type_names += common_sampler_type_to_str(sampler) + ";";
}
sampler_type_names.pop_back();
if (!sampler_type_names.empty()) {
sampler_type_names.pop_back(); // remove last semicolon
}
/**
@ -1127,7 +1149,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
add_opt(common_arg(
{"--cache-ram", "-cram"}, "N",
{"-cram", "--cache-ram"}, "N",
string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)"
"[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)", params.cache_ram_mib),
[](common_params & params, int value) {
@ -1135,12 +1157,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
add_opt(common_arg(
{"--kv-unified", "-kvu"},
{"-kvu", "--kv-unified"},
"use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)",
[](common_params & params) {
params.kv_unified = true;
}
).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER}));
).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_PERPLEXITY}));
add_opt(common_arg(
{"--context-shift"},
{"--no-context-shift"},
@ -1184,7 +1206,7 @@ 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_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION}));
).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION, LLAMA_EXAMPLE_MTMD}));
add_opt(common_arg(
{"--perf"},
{"--no-perf"},
@ -1226,13 +1248,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION}));
add_opt(common_arg(
{"--in-file"}, "FNAME",
"an input file (repeat to specify multiple files)",
"an input file (use comma-separated values to specify multiple files)",
[](common_params & params, const std::string & value) {
std::ifstream file(value);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
for (const auto & item : string_split<std::string>(value, ',')) {
std::ifstream file(item);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
}
params.in_files.push_back(item);
}
params.in_files.push_back(value);
}
).set_examples({LLAMA_EXAMPLE_IMATRIX}));
add_opt(common_arg(
@ -1401,7 +1425,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_sparam());
add_opt(common_arg(
{"--sampling-seq", "--sampler-seq"}, "SEQUENCE",
{"--sampler-seq", "--sampling-seq"}, "SEQUENCE",
string_format("simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str()),
[](common_params & params, const std::string & value) {
params.sampling.samplers = common_sampler_types_from_chars(value);
@ -1969,9 +1993,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_OFFLOAD"));
add_opt(common_arg(
{"--image", "--audio"}, "FILE",
"path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n",
"path to an image or audio file. use with multimodal models, use comma-separated values for multiple files\n",
[](common_params & params, const std::string & value) {
params.image.emplace_back(value);
for (const auto & item : string_split<std::string>(value, ',')) {
params.image.emplace_back(item);
}
}
).set_examples({LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_CLI}));
add_opt(common_arg(
@ -2057,26 +2083,26 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
));
add_opt(common_arg(
{"--override-tensor", "-ot"}, "<tensor name pattern>=<buffer type>,...",
{"-ot", "--override-tensor"}, "<tensor name pattern>=<buffer type>,...",
"override tensor buffer type", [](common_params & params, const std::string & value) {
parse_tensor_buffer_overrides(value, params.tensor_buft_overrides);
}
));
add_opt(common_arg(
{"--override-tensor-draft", "-otd"}, "<tensor name pattern>=<buffer type>,...",
{"-otd", "--override-tensor-draft"}, "<tensor name pattern>=<buffer type>,...",
"override tensor buffer type for draft model", [](common_params & params, const std::string & value) {
parse_tensor_buffer_overrides(value, params.speculative.tensor_buft_overrides);
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
add_opt(common_arg(
{"--cpu-moe", "-cmoe"},
{"-cmoe", "--cpu-moe"},
"keep all Mixture of Experts (MoE) weights in the CPU",
[](common_params & params) {
params.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
}
).set_env("LLAMA_ARG_CPU_MOE"));
add_opt(common_arg(
{"--n-cpu-moe", "-ncmoe"}, "N",
{"-ncmoe", "--n-cpu-moe"}, "N",
"keep the Mixture of Experts (MoE) weights of the first N layers in the CPU",
[](common_params & params, int value) {
if (value < 0) {
@ -2091,14 +2117,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_env("LLAMA_ARG_N_CPU_MOE"));
add_opt(common_arg(
{"--cpu-moe-draft", "-cmoed"},
{"-cmoed", "--cpu-moe-draft"},
"keep all Mixture of Experts (MoE) weights in the CPU for the draft model",
[](common_params & params) {
params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
add_opt(common_arg(
{"--n-cpu-moe-draft", "-ncmoed"}, "N",
{"-ncmoed", "--n-cpu-moe-draft"}, "N",
"keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model",
[](common_params & params, int value) {
if (value < 0) {
@ -2218,12 +2244,39 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
));
add_opt(common_arg(
{"--override-kv"}, "KEY=TYPE:VALUE",
"advanced option to override model metadata by key. may be specified multiple times.\n"
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false",
{"--override-kv"}, "KEY=TYPE:VALUE,...",
"advanced option to override model metadata by key. to specify multiple overrides, either use comma-separated or repeat this argument.\n"
"types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false,tokenizer.ggml.add_eos_token=bool:false",
[](common_params & params, const std::string & value) {
if (!string_parse_kv_override(value.c_str(), params.kv_overrides)) {
throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", value.c_str()));
std::vector<std::string> kv_overrides;
std::string current;
bool escaping = false;
for (const char c : value) {
if (escaping) {
current.push_back(c);
escaping = false;
} else if (c == '\\') {
escaping = true;
} else if (c == ',') {
kv_overrides.push_back(current);
current.clear();
} else {
current.push_back(c);
}
}
if (escaping) {
current.push_back('\\');
}
kv_overrides.push_back(current);
for (const auto & kv_override : kv_overrides) {
if (!string_parse_kv_override(kv_override.c_str(), params.kv_overrides)) {
throw std::runtime_error(string_format("error: Invalid type for KV override: %s\n", kv_override.c_str()));
}
}
}
));
@ -2237,33 +2290,50 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
));
add_opt(common_arg(
{"--lora"}, "FNAME",
"path to LoRA adapter (can be repeated to use multiple adapters)",
"path to LoRA adapter (use comma-separated values to load multiple adapters)",
[](common_params & params, const std::string & value) {
params.lora_adapters.push_back({ std::string(value), 1.0, "", "", nullptr });
for (const auto & item : string_split<std::string>(value, ',')) {
params.lora_adapters.push_back({ item, 1.0, "", "", nullptr });
}
}
// we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
add_opt(common_arg(
{"--lora-scaled"}, "FNAME", "SCALE",
"path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters)",
[](common_params & params, const std::string & fname, const std::string & scale) {
params.lora_adapters.push_back({ fname, std::stof(scale), "", "", nullptr });
{"--lora-scaled"}, "FNAME:SCALE,...",
"path to LoRA adapter with user defined scaling (format: FNAME:SCALE,...)\n"
"note: use comma-separated values",
[](common_params & params, const std::string & value) {
for (const auto & item : string_split<std::string>(value, ',')) {
auto parts = string_split<std::string>(item, ':');
if (parts.size() != 2) {
throw std::invalid_argument("lora-scaled format: FNAME:SCALE");
}
params.lora_adapters.push_back({ parts[0], std::stof(parts[1]), "", "", nullptr });
}
}
// we define this arg on both COMMON and EXPORT_LORA, so when showing help message of export-lora, it will be categorized as "example-specific" arg
).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_EXPORT_LORA}));
add_opt(common_arg(
{"--control-vector"}, "FNAME",
"add a control vector\nnote: this argument can be repeated to add multiple control vectors",
"add a control vector\nnote: use comma-separated values to add multiple control vectors",
[](common_params & params, const std::string & value) {
params.control_vectors.push_back({ 1.0f, value, });
for (const auto & item : string_split<std::string>(value, ',')) {
params.control_vectors.push_back({ 1.0f, item, });
}
}
));
add_opt(common_arg(
{"--control-vector-scaled"}, "FNAME", "SCALE",
{"--control-vector-scaled"}, "FNAME:SCALE,...",
"add a control vector with user defined scaling SCALE\n"
"note: this argument can be repeated to add multiple scaled control vectors",
[](common_params & params, const std::string & fname, const std::string & scale) {
params.control_vectors.push_back({ std::stof(scale), fname });
"note: use comma-separated values (format: FNAME:SCALE,...)",
[](common_params & params, const std::string & value) {
for (const auto & item : string_split<std::string>(value, ',')) {
auto parts = string_split<std::string>(item, ':');
if (parts.size() != 2) {
throw std::invalid_argument("control-vector-scaled format: FNAME:SCALE");
}
params.control_vectors.push_back({ std::stof(parts[1]), parts[0] });
}
}
));
add_opt(common_arg(
@ -2353,13 +2423,15 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
).set_env("HF_TOKEN"));
add_opt(common_arg(
{"--context-file"}, "FNAME",
"file to load context from (repeat to specify multiple files)",
"file to load context from (use comma-separated values to specify multiple files)",
[](common_params & params, const std::string & value) {
std::ifstream file(value, std::ios::binary);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", value.c_str()));
for (const auto & item : string_split<std::string>(value, ',')) {
std::ifstream file(item, std::ios::binary);
if (!file) {
throw std::runtime_error(string_format("error: failed to open file '%s'\n", item.c_str()));
}
params.context_files.push_back(item);
}
params.context_files.push_back(value);
}
).set_examples({LLAMA_EXAMPLE_RETRIEVAL}));
add_opt(common_arg(
@ -2550,6 +2622,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.api_prefix = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX"));
add_opt(common_arg(
{"--webui-config"}, "JSON",
"JSON that provides default WebUI settings (overrides WebUI defaults)",
[](common_params & params, const std::string & value) {
params.webui_config_json = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG"));
add_opt(common_arg(
{"--webui-config-file"}, "PATH",
"JSON file that provides default WebUI settings (overrides WebUI defaults)",
[](common_params & params, const std::string & value) {
params.webui_config_json = read_file(value);
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI_CONFIG_FILE"));
add_opt(common_arg(
{"--webui"},
{"--no-webui"},
@ -2566,7 +2652,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_EMBEDDINGS"));
add_opt(common_arg(
{"--reranking", "--rerank"},
{"--rerank", "--reranking"},
string_format("enable reranking endpoint on server (default: %s)", "disabled"),
[](common_params & params) {
params.embedding = true;
@ -2801,6 +2887,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
params.lora_init_without_apply = true;
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--sleep-idle-seconds"}, "SECONDS",
string_format("number of seconds of idleness after which the server will sleep (default: %d; -1 = disabled)", params.sleep_idle_seconds),
[](common_params & params, int value) {
if (value == 0 || value < -1) {
throw std::invalid_argument("invalid value: cannot be 0 or less than -1");
}
params.sleep_idle_seconds = value;
}
).set_examples({LLAMA_EXAMPLE_SERVER}));
add_opt(common_arg(
{"--simple-io"},
"use basic IO for better compatibility in subprocesses and limited consoles",
@ -3037,7 +3133,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
}
).set_examples({LLAMA_EXAMPLE_SPECULATIVE}));
add_opt(common_arg(
{"--draft-max", "--draft", "--draft-n"}, "N",
{"--draft", "--draft-n", "--draft-max"}, "N",
string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.n_max),
[](common_params & params, int value) {
params.speculative.n_max = value;
@ -3413,3 +3509,24 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
return ctx_arg;
}
void common_params_add_preset_options(std::vector<common_arg> & args) {
// arguments below won't be treated as CLI args, only preset options
args.push_back(common_arg(
{"load-on-startup"}, "NAME",
"in server router mode, autoload this model on startup",
[](common_params &, const std::string &) { /* unused */ }
).set_env(COMMON_ARG_PRESET_LOAD_ON_STARTUP).set_preset_only());
// args.push_back(common_arg(
// {"pin"},
// "in server router mode, do not unload this model if models_max is exceeded",
// [](common_params &) { /* unused */ }
// ).set_preset_only());
// args.push_back(common_arg(
// {"unload-idle-seconds"}, "SECONDS",
// "in server router mode, unload models idle for more than this many seconds",
// [](common_params &, int) { /* unused */ }
// ).set_preset_only());
}

11
common/arg.h
View File

@ -8,6 +8,9 @@
#include <vector>
#include <cstring>
// pseudo-env variable to identify preset-only arguments
#define COMMON_ARG_PRESET_LOAD_ON_STARTUP "__PRESET_LOAD_ON_STARTUP"
//
// CLI argument parsing
//
@ -22,6 +25,7 @@ struct common_arg {
const char * env = nullptr;
std::string help;
bool is_sparam = false; // is current arg a sampling param?
bool is_preset_only = false; // is current arg preset-only (not treated as CLI arg)
void (*handler_void) (common_params & params) = nullptr;
void (*handler_string) (common_params & params, const std::string &) = nullptr;
void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr;
@ -70,6 +74,7 @@ struct common_arg {
common_arg & set_excludes(std::initializer_list<enum llama_example> excludes);
common_arg & set_env(const char * env);
common_arg & set_sparam();
common_arg & set_preset_only();
bool in_example(enum llama_example ex);
bool is_exclude(enum llama_example ex);
bool get_value_from_env(std::string & output) const;
@ -114,9 +119,13 @@ struct common_params_context {
bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
// parse input arguments from CLI into a map
// TODO: support repeated args in the future
bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map);
// populate preset-only arguments
// these arguments are not treated as command line arguments
// see: https://github.com/ggml-org/llama.cpp/issues/18163
void common_params_add_preset_options(std::vector<common_arg> & args);
// initialize argument parser context - used by test-arg-parser and preset
common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);

2
common/common.cpp
View File

@ -1092,7 +1092,7 @@ common_init_result::common_init_result(common_params & params) :
auto cparams = common_context_params_to_llama(params);
if (params.fit_params) {
LOG_INF("%s: fitting params to device memory, to report bugs during this step use -fit off (or --verbose if you can't)\n", __func__);
LOG_INF("%s: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on\n", __func__);
llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx,
params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);

8
common/common.h
View File

@ -475,7 +475,8 @@ struct common_params {
bool enable_chat_template = true;
common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
int reasoning_budget = -1;
bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response
bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response
int sleep_idle_seconds = -1; // if >0, server will sleep after this many seconds of idle time
std::vector<std::string> api_keys;
@ -484,8 +485,11 @@ struct common_params {
std::map<std::string, std::string> default_template_kwargs;
// webui configs
bool webui = true;
std::string webui_config_json;
// "advanced" endpoints are disabled by default for better security
bool webui = true;
bool endpoint_slots = true;
bool endpoint_props = false; // only control POST requests, not GET
bool endpoint_metrics = false;

202
common/preset.cpp
View File

@ -2,6 +2,7 @@
#include "preset.h"
#include "peg-parser.h"
#include "log.h"
#include "download.h"
#include <fstream>
#include <sstream>
@ -15,11 +16,22 @@ static std::string rm_leading_dashes(const std::string & str) {
return str.substr(pos);
}
std::vector<std::string> common_preset::to_args() const {
std::vector<std::string> common_preset::to_args(const std::string & bin_path) const {
std::vector<std::string> args;
if (!bin_path.empty()) {
args.push_back(bin_path);
}
for (const auto & [opt, value] : options) {
args.push_back(opt.args.back()); // use the last arg as the main arg
if (opt.is_preset_only) {
continue; // skip preset-only options (they are not CLI args)
}
// use the last arg as the main arg (i.e. --long-form)
args.push_back(opt.args.back());
// handle value(s)
if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) {
// flag option, no value
if (common_arg_utils::is_falsey(value)) {
@ -63,6 +75,52 @@ std::string common_preset::to_ini() const {
return ss.str();
}
void common_preset::set_option(const common_preset_context & ctx, const std::string & env, const std::string & value) {
// try if option exists, update it
for (auto & [opt, val] : options) {
if (opt.env && env == opt.env) {
val = value;
return;
}
}
// if option does not exist, we need to add it
if (ctx.key_to_opt.find(env) == ctx.key_to_opt.end()) {
throw std::runtime_error(string_format(
"%s: option with env '%s' not found in ctx_params",
__func__, env.c_str()
));
}
options[ctx.key_to_opt.at(env)] = value;
}
void common_preset::unset_option(const std::string & env) {
for (auto it = options.begin(); it != options.end(); ) {
const common_arg & opt = it->first;
if (opt.env && env == opt.env) {
it = options.erase(it);
return;
} else {
++it;
}
}
}
bool common_preset::get_option(const std::string & env, std::string & value) const {
for (const auto & [opt, val] : options) {
if (opt.env && env == opt.env) {
value = val;
return true;
}
}
return false;
}
void common_preset::merge(const common_preset & other) {
for (const auto & [opt, val] : other.options) {
options[opt] = val; // overwrite existing options
}
}
static std::map<std::string, std::map<std::string, std::string>> parse_ini_from_file(const std::string & path) {
std::map<std::string, std::map<std::string, std::string>> parsed;
@ -172,9 +230,14 @@ static std::string parse_bool_arg(const common_arg & arg, const std::string & ke
return value;
}
common_presets common_presets_load(const std::string & path, common_params_context & ctx_params) {
common_preset_context::common_preset_context(llama_example ex)
: ctx_params(common_params_parser_init(default_params, ex)) {
common_params_add_preset_options(ctx_params.options);
key_to_opt = get_map_key_opt(ctx_params);
}
common_presets common_preset_context::load_from_ini(const std::string & path, common_preset & global) const {
common_presets out;
auto key_to_opt = get_map_key_opt(ctx_params);
auto ini_data = parse_ini_from_file(path);
for (auto section : ini_data) {
@ -188,7 +251,7 @@ common_presets common_presets_load(const std::string & path, common_params_conte
for (const auto & [key, value] : section.second) {
LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str());
if (key_to_opt.find(key) != key_to_opt.end()) {
auto & opt = key_to_opt[key];
const auto & opt = key_to_opt.at(key);
if (is_bool_arg(opt)) {
preset.options[opt] = parse_bool_arg(opt, key, value);
} else {
@ -199,8 +262,137 @@ common_presets common_presets_load(const std::string & path, common_params_conte
// TODO: maybe warn about unknown key?
}
}
if (preset.name == "*") {
// handle global preset
global = preset;
} else {
out[preset.name] = preset;
}
}
return out;
}
common_presets common_preset_context::load_from_cache() const {
common_presets out;
auto cached_models = common_list_cached_models();
for (const auto & model : cached_models) {
common_preset preset;
preset.name = model.to_string();
preset.set_option(*this, "LLAMA_ARG_HF_REPO", model.to_string());
out[preset.name] = preset;
}
return out;
}
struct local_model {
std::string name;
std::string path;
std::string path_mmproj;
};
common_presets common_preset_context::load_from_models_dir(const std::string & models_dir) const {
if (!std::filesystem::exists(models_dir) || !std::filesystem::is_directory(models_dir)) {
throw std::runtime_error(string_format("error: '%s' does not exist or is not a directory\n", models_dir.c_str()));
}
std::vector<local_model> models;
auto scan_subdir = [&models](const std::string & subdir_path, const std::string & name) {
auto files = fs_list(subdir_path, false);
common_file_info model_file;
common_file_info first_shard_file;
common_file_info mmproj_file;
for (const auto & file : files) {
if (string_ends_with(file.name, ".gguf")) {
if (file.name.find("mmproj") != std::string::npos) {
mmproj_file = file;
} else if (file.name.find("-00001-of-") != std::string::npos) {
first_shard_file = file;
} else {
model_file = file;
}
}
}
// single file model
local_model model{
/* name */ name,
/* path */ first_shard_file.path.empty() ? model_file.path : first_shard_file.path,
/* path_mmproj */ mmproj_file.path // can be empty
};
if (!model.path.empty()) {
models.push_back(model);
}
};
auto files = fs_list(models_dir, true);
for (const auto & file : files) {
if (file.is_dir) {
scan_subdir(file.path, file.name);
} else if (string_ends_with(file.name, ".gguf")) {
// single file model
std::string name = file.name;
string_replace_all(name, ".gguf", "");
local_model model{
/* name */ name,
/* path */ file.path,
/* path_mmproj */ ""
};
models.push_back(model);
}
}
// convert local models to presets
common_presets out;
for (const auto & model : models) {
common_preset preset;
preset.name = model.name;
preset.set_option(*this, "LLAMA_ARG_MODEL", model.path);
if (!model.path_mmproj.empty()) {
preset.set_option(*this, "LLAMA_ARG_MMPROJ", model.path_mmproj);
}
out[preset.name] = preset;
}
return out;
}
common_preset common_preset_context::load_from_args(int argc, char ** argv) const {
common_preset preset;
preset.name = COMMON_PRESET_DEFAULT_NAME;
bool ok = common_params_to_map(argc, argv, ctx_params.ex, preset.options);
if (!ok) {
throw std::runtime_error("failed to parse CLI arguments into preset");
}
return preset;
}
common_presets common_preset_context::cascade(const common_presets & base, const common_presets & added) const {
common_presets out = base; // copy
for (const auto & [name, preset_added] : added) {
if (out.find(name) != out.end()) {
// if exists, merge
common_preset & target = out[name];
target.merge(preset_added);
} else {
// otherwise, add directly
out[name] = preset_added;
}
}
return out;
}
common_presets common_preset_context::cascade(const common_preset & base, const common_presets & presets) const {
common_presets out;
for (const auto & [name, preset] : presets) {
common_preset tmp = base; // copy
tmp.name = name;
tmp.merge(preset);
out[name] = std::move(tmp);
}
return out;
}

48
common/preset.h
View File

@ -13,20 +13,62 @@
constexpr const char * COMMON_PRESET_DEFAULT_NAME = "default";
struct common_preset_context;
struct common_preset {
std::string name;
// TODO: support repeated args in the future
// options are stored as common_arg to string mapping, representing CLI arg and its value
std::map<common_arg, std::string> options;
// convert preset to CLI argument list
std::vector<std::string> to_args() const;
std::vector<std::string> to_args(const std::string & bin_path = "") const;
// convert preset to INI format string
std::string to_ini() const;
// TODO: maybe implement to_env() if needed
// modify preset options where argument is identified by its env variable
void set_option(const common_preset_context & ctx, const std::string & env, const std::string & value);
// unset option by its env variable
void unset_option(const std::string & env);
// get option value by its env variable, return false if not found
bool get_option(const std::string & env, std::string & value) const;
// merge another preset into this one, overwriting existing options
void merge(const common_preset & other);
};
// interface for multiple presets in one file
using common_presets = std::map<std::string, common_preset>;
common_presets common_presets_load(const std::string & path, common_params_context & ctx_params);
// context for loading and editing presets
struct common_preset_context {
common_params default_params; // unused for now
common_params_context ctx_params;
std::map<std::string, common_arg> key_to_opt;
common_preset_context(llama_example ex);
// load presets from INI file
common_presets load_from_ini(const std::string & path, common_preset & global) const;
// generate presets from cached models
common_presets load_from_cache() const;
// generate presets from local models directory
// for the directory structure, see "Using multiple models" in server/README.md
common_presets load_from_models_dir(const std::string & models_dir) const;
// generate one preset from CLI arguments
common_preset load_from_args(int argc, char ** argv) const;
// cascade multiple presets if exist on both: base < added
// if preset does not exist in base, it will be added without modification
common_presets cascade(const common_presets & base, const common_presets & added) const;
// apply presets over a base preset (same idea as CSS cascading)
common_presets cascade(const common_preset & base, const common_presets & presets) const;
};

88
common/sampling.cpp
View File

@ -104,10 +104,9 @@ struct ring_buffer {
struct common_sampler {
common_params_sampling params;
struct llama_sampler * grmr;
struct llama_sampler * chain;
bool grammar;
ring_buffer<llama_token> prev;
std::vector<llama_token_data> cur;
@ -167,15 +166,14 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
lparams.no_perf = params.no_perf;
llama_sampler * grmr = nullptr;
llama_sampler * chain = llama_sampler_chain_init(lparams);
bool grammar = false;
std::vector<llama_sampler *> samplers;
if (params.grammar.compare(0, 11, "%llguidance") == 0) {
#ifdef LLAMA_USE_LLGUIDANCE
samplers.push_back(llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()));
grammar = true;
grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
#else
GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
#endif // LLAMA_USE_LLGUIDANCE
@ -224,15 +222,12 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
if (!params.grammar.empty()) {
if (params.grammar_lazy) {
samplers.push_back(
llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
trigger_patterns_c.data(), trigger_patterns_c.size(),
trigger_tokens.data(), trigger_tokens.size()));
grmr = llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
trigger_patterns_c.data(), trigger_patterns_c.size(),
trigger_tokens.data(), trigger_tokens.size());
} else {
samplers.push_back(llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"));
grmr = llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
}
grammar = true;
}
}
@ -303,8 +298,8 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
auto * result = new common_sampler {
/* .params = */ params,
/* .grmr = */ grmr,
/* .chain = */ chain,
/* .grammar = */ grammar,
/* .prev = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
/* .cur = */ {},
/* .cur_p = */ {},
@ -315,6 +310,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
void common_sampler_free(struct common_sampler * gsmpl) {
if (gsmpl) {
llama_sampler_free(gsmpl->grmr);
llama_sampler_free(gsmpl->chain);
delete gsmpl;
@ -324,25 +320,12 @@ void common_sampler_free(struct common_sampler * gsmpl) {
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
const auto tm = gsmpl->tm();
if (gsmpl->grammar) {
const int n_smpl = llama_sampler_chain_n(gsmpl->chain);
for (int i = 0; i < n_smpl; i++) {
auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
// the grammar sampler is always the first one
if (i == 0) {
if (accept_grammar) {
llama_sampler_accept(smpl, token);
}
} else {
llama_sampler_accept(smpl, token);
}
}
} else {
llama_sampler_accept(gsmpl->chain, token);
if (gsmpl->grmr && accept_grammar) {
llama_sampler_accept(gsmpl->grmr, token);
}
llama_sampler_accept(gsmpl->chain, token);
gsmpl->prev.push_back(token);
}
@ -353,8 +336,8 @@ void common_sampler_reset(struct common_sampler * gsmpl) {
struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
return new common_sampler {
/* .params = */ gsmpl->params,
/* .grmr = */ llama_sampler_clone(gsmpl->grmr),
/* .chain = */ llama_sampler_clone(gsmpl->chain),
/* .grammar = */ gsmpl->grammar,
/* .prev = */ gsmpl->prev,
/* .cur = */ gsmpl->cur,
/* .cur_p = */ gsmpl->cur_p,
@ -410,7 +393,7 @@ struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) {
return gsmpl->chain;
}
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx) {
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
llama_synchronize(ctx);
// start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations
@ -418,11 +401,42 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
llama_token id = LLAMA_TOKEN_NULL;
auto & grmr = gsmpl->grmr;
auto & chain = gsmpl->chain;
auto & cur_p = gsmpl->cur_p; // initialized by set_logits
gsmpl->set_logits(ctx, idx);
if (grammar_first) {
llama_sampler_apply(grmr, &cur_p);
}
llama_sampler_apply(chain, &cur_p);
id = cur_p.data[cur_p.selected].id;
if (grammar_first) {
return id;
}
// check if it the sampled token fits the grammar (grammar-based rejection sampling)
{
llama_token_data single_token_data = { id, 1.0f, 0.0f };
llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
llama_sampler_apply(grmr, &single_token_data_array);
const bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
if (is_valid) {
return id;
}
}
// resampling:
// if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
gsmpl->set_logits(ctx, idx);
llama_sampler_apply(grmr, &cur_p);
llama_sampler_apply(chain, &cur_p);
GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
@ -432,7 +446,7 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
return id;
}
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft) {
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
std::vector<llama_token> result;
@ -440,7 +454,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
size_t i = 0;
for (; i < draft.size(); i++) {
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
common_sampler_accept(gsmpl, id, true);
@ -452,7 +466,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
}
if (i == draft.size()) {
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
common_sampler_accept(gsmpl, id, true);
@ -462,13 +476,13 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
return result;
}
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft) {
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
std::vector<int> idxs(draft.size() + 1);
for (size_t i = 0; i < idxs.size(); ++i) {
idxs[i] = i;
}
return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft);
return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
}
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {

9
common/sampling.h
View File

@ -57,7 +57,10 @@ struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl);
// - check if the token fits the grammar (if any)
// - if not: resample by first applying the grammar constraints and then sampling again (slower path)
//
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx);
// if grammar_first is true, the grammar is applied before the samplers (slower)
// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
//
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
// generalized version of common_sampler_sample
//
@ -75,10 +78,10 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
//
// returns at least 1 token, up to idxs.size()
//
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft);
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
// assume idxs == [ 0, 1, 2, ..., draft.size() ]
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft);
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);

2
common/speculative.cpp
View File

@ -315,7 +315,7 @@ llama_tokens common_speculative_gen_draft(
for (int i = 0; i < params.n_draft; ++i) {
common_batch_clear(batch);
common_sampler_sample(smpl, ctx_dft, 0);
common_sampler_sample(smpl, ctx_dft, 0, true);
const auto * cur_p = common_sampler_get_candidates(smpl, true);

101
convert_hf_to_gguf.py
View File

@ -189,10 +189,10 @@ class ModelBase:
return tensors
prefix = "model" if not self.is_mistral_format else "consolidated"
part_names: set[str] = set(ModelBase.get_model_part_names(self.dir_model, prefix, ".safetensors"))
part_names: list[str] = ModelBase.get_model_part_names(self.dir_model, prefix, ".safetensors")
is_safetensors: bool = len(part_names) > 0
if not is_safetensors:
part_names = set(ModelBase.get_model_part_names(self.dir_model, "pytorch_model", ".bin"))
part_names = ModelBase.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
tensor_names_from_index: set[str] = set()
@ -209,7 +209,8 @@ class ModelBase:
if weight_map is None or not isinstance(weight_map, dict):
raise ValueError(f"Can't load 'weight_map' from {index_name!r}")
tensor_names_from_index.update(weight_map.keys())
part_names |= set(weight_map.values())
part_dict: dict[str, None] = dict.fromkeys(weight_map.values(), None)
part_names = sorted(part_dict.keys())
else:
weight_map = {}
else:
@ -711,6 +712,9 @@ class ModelBase:
if "thinker_config" in config:
# rename for Qwen2.5-Omni
config["text_config"] = config["thinker_config"]["text_config"]
if "lfm" in config:
# rename for LFM2-Audio
config["text_config"] = config["lfm"]
return config
@classmethod
@ -1838,7 +1842,7 @@ class MmprojModel(ModelBase):
def tensor_force_quant(self, name, new_name, bid, n_dims):
del bid, name, n_dims # unused
if ".patch_embd.weight" in new_name:
if ".patch_embd.weight" in new_name or ".patch_merger.weight" in new_name:
return gguf.GGMLQuantizationType.F16 if self.ftype == gguf.LlamaFileType.MOSTLY_F16 else gguf.GGMLQuantizationType.F32
return False
@ -9712,12 +9716,12 @@ class LFM2Model(TextModel):
self._add_feed_forward_length()
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
is_vision_tensor = "vision_tower" in name or "multi_modal_projector" in name
if is_vision_tensor:
# skip vision tensors
if self._is_vision_tensor(name) or self._is_audio_tensor(name):
# skip multimodal tensors
return []
name = name.replace("language_model.", "")
name = name.replace("language_model.", "") # vision
name = name.replace("lfm.", "model.") # audio
# conv op requires 2d tensor
if 'conv.conv' in name:
@ -9725,6 +9729,12 @@ class LFM2Model(TextModel):
return [(self.map_tensor_name(name), data_torch)]
def _is_vision_tensor(self, name: str) -> bool:
return "vision_tower" in name or "multi_modal_projector" in name
def _is_audio_tensor(self, name: str):
return any(p in name for p in ["audio", "codebook", "conformer", "depth_embedding", "depthformer", "depth_linear"])
@ModelBase.register("Lfm2MoeForCausalLM")
class LFM2MoeModel(TextModel):
@ -9830,6 +9840,81 @@ class LFM2VLModel(MmprojModel):
return [] # skip other tensors
@ModelBase.register("Lfm2AudioForConditionalGeneration")
class LFM2AudioModel(MmprojModel):
has_vision_encoder = False
has_audio_encoder = True
model_name = "Lfm2AudioEncoder"
_batch_norm_tensors: list[dict[str, Tensor]] | None = None
def get_audio_config(self) -> dict[str, Any] | None:
return self.global_config.get("encoder")
def set_gguf_parameters(self):
assert self.hparams_audio is not None
self.hparams_audio["hidden_size"] = self.hparams_audio["d_model"]
self.hparams_audio["intermediate_size"] = self.hparams_audio["d_model"]
self.hparams_audio["num_attention_heads"] = self.hparams_audio["n_heads"]
super().set_gguf_parameters()
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.LFM2A)
self.gguf_writer.add_audio_num_mel_bins(self.hparams_audio["feat_in"])
self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
def tensor_force_quant(self, name, new_name, bid, n_dims):
if ".conv" in name and ".weight" in name:
return gguf.GGMLQuantizationType.F32
return super().tensor_force_quant(name, new_name, bid, n_dims)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# skip language model tensors
if name.startswith("lfm."):
return []
# for training only
if any(p in name for p in ["audio_loss_weight"]):
return []
# for audio output
if any(p in name for p in ["codebook_offsets", "depth_embeddings", "depth_linear", "depthformer"]):
return []
# fold running_mean, running_var and eps into weight and bias for batch_norm
if "batch_norm" in name:
if self._batch_norm_tensors is None:
self._batch_norm_tensors = [{} for _ in range(self.block_count)]
assert bid is not None
self._batch_norm_tensors[bid][name] = data_torch
if len(self._batch_norm_tensors[bid]) < 5:
return []
weight = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.weight"]
bias = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.bias"]
running_mean = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_mean"]
running_var = self._batch_norm_tensors[bid][f"conformer.layers.{bid}.conv.batch_norm.running_var"]
eps = 1e-5 # default value
a = weight / torch.sqrt(running_var + eps)
b = bias - running_mean * a
return [
(self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.weight"), a),
(self.map_tensor_name(f"conformer.layers.{bid}.conv.batch_norm.bias"), b),
]
# reshape conv weights
if name.startswith("conformer.pre_encode.conv.") and name.endswith(".bias"):
data_torch = data_torch[:, None, None]
if "conv.depthwise_conv" in name and name.endswith(".weight"):
assert data_torch.shape[1] == 1
data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[2])
if "conv.pointwise_conv" in name and name.endswith(".weight"):
assert data_torch.shape[2] == 1
data_torch = data_torch.reshape(data_torch.shape[0], data_torch.shape[1])
return [(self.map_tensor_name(name), data_torch)]
@ModelBase.register("SmallThinkerForCausalLM")
class SmallThinkerModel(TextModel):
model_arch = gguf.MODEL_ARCH.SMALLTHINKER

24
docs/android.md
View File

@ -1,7 +1,27 @@
# Android
## Build on Android using Termux
## Build GUI binding using Android Studio
Import the `examples/llama.android` directory into Android Studio, then perform a Gradle sync and build the project.
![Project imported into Android Studio](./android/imported-into-android-studio.jpg)
This Android binding supports hardware acceleration up to `SME2` for **Arm** and `AMX` for **x86-64** CPUs on Android and ChromeOS devices.
It automatically detects the host's hardware to load compatible kernels. As a result, it runs seamlessly on both the latest premium devices and older devices that may lack modern CPU features or have limited RAM, without requiring any manual configuration.
A minimal Android app frontend is included to showcase the binding’s core functionalities:
1. **Parse GGUF metadata** via `GgufMetadataReader` from either a `ContentResolver` provided `Uri` from shared storage, or a local `File` from your app's private storage.
2. **Obtain a `InferenceEngine`** instance through the `AiChat` facade and load your selected model via its app-private file path.
3. **Send a raw user prompt** for automatic template formatting, prefill, and batch decoding. Then collect the generated tokens in a Kotlin `Flow`.
For a production-ready experience that leverages advanced features such as system prompts and benchmarks, plus friendly UI features such as model management and Arm feature visualizer, check out [Arm AI Chat](https://play.google.com/store/apps/details?id=com.arm.aichat) on Google Play.
This project is made possible through a collaborative effort by Arm's **CT-ML**, **CE-ML** and **STE** groups:
| ![Home screen](https://naco-siren.github.io/ai-chat/policy/index/1-llm-starter-pack.png) | ![System prompt](https://naco-siren.github.io/ai-chat/policy/index/5-system-prompt.png) | !["Haiku"](https://naco-siren.github.io/ai-chat/policy/index/4-metrics.png) |
|:------------------------------------------------------:|:----------------------------------------------------:|:--------------------------------------------------------:|
| Home screen | System prompt | "Haiku" |
## Build CLI on Android using Termux
[Termux](https://termux.dev/en/) is an Android terminal emulator and Linux environment app (no root required). As of writing, Termux is available experimentally in the Google Play Store; otherwise, it may be obtained directly from the project repo or on F-Droid.
@ -32,7 +52,7 @@ To see what it might look like visually, here's an old demo of an interactive se
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
## Cross-compile using Android NDK
## Cross-compile CLI using Android NDK
It's possible to build `llama.cpp` for Android on your host system via CMake and the Android NDK. If you are interested in this path, ensure you already have an environment prepared to cross-compile programs for Android (i.e., install the Android SDK). Note that, unlike desktop environments, the Android environment ships with a limited set of native libraries, and so only those libraries are available to CMake when building with the Android NDK (see: https://developer.android.com/ndk/guides/stable_apis.)
Once you're ready and have cloned `llama.cpp`, invoke the following in the project directory:

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2
docs/backend/hexagon/CMakeUserPresets.json
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@ -22,6 +22,7 @@
"GGML_LLAMAFILE": "OFF",
"GGML_OPENCL": "ON",
"GGML_HEXAGON": "ON",
"GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
"LLAMA_CURL": "OFF"
}
},
@ -36,6 +37,7 @@
"GGML_LLAMAFILE": "OFF",
"GGML_OPENCL": "ON",
"GGML_HEXAGON": "ON",
"GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
"LLAMA_CURL": "OFF"
}
},

4
docs/development/parsing.md
View File

@ -55,7 +55,7 @@ auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder &
```
For a more complete example, see `test_example_native()` in
[tests/test-chat-peg-parser.cpp](tests/test-chat-peg-parser.cpp).
[tests/test-chat-peg-parser.cpp](/tests/test-chat-peg-parser.cpp).
## Parsers/Combinators
@ -175,7 +175,7 @@ Most model output can be placed in one of the following categories:
(Qwen3-Coder, MiniMax M2) or pseudo-function calls (LFM2)
To provide broad coverage,
[`common/chat-peg-parser.h`](common/chat-peg-parser.h) contains builders and
[`common/chat-peg-parser.h`](/common/chat-peg-parser.h) contains builders and
mappers that help create parsers and visitors/extractors for these types. They
require parsers to tag nodes to conform to an AST "shape". This normalization
makes it easy to extract information and generalize parsing.

0
examples/llama.android/README.md
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52
examples/llama.android/app/build.gradle.kts
View File

@ -1,16 +1,18 @@
plugins {
id("com.android.application")
id("org.jetbrains.kotlin.android")
alias(libs.plugins.android.application)
alias(libs.plugins.jetbrains.kotlin.android)
}
android {
namespace = "com.example.llama"
compileSdk = 34
compileSdk = 36
defaultConfig {
applicationId = "com.example.llama"
applicationId = "com.example.llama.aichat"
minSdk = 33
targetSdk = 34
targetSdk = 36
versionCode = 1
versionName = "1.0"
@ -21,8 +23,17 @@ android {
}
buildTypes {
debug {
isMinifyEnabled = true
isShrinkResources = true
proguardFiles(
getDefaultProguardFile("proguard-android.txt"),
"proguard-rules.pro"
)
}
release {
isMinifyEnabled = false
isMinifyEnabled = true
isShrinkResources = true
proguardFiles(
getDefaultProguardFile("proguard-android-optimize.txt"),
"proguard-rules.pro"
@ -36,30 +47,15 @@ android {
kotlinOptions {
jvmTarget = "1.8"
}
buildFeatures {
compose = true
}
composeOptions {
kotlinCompilerExtensionVersion = "1.5.1"
}
}
dependencies {
implementation(libs.bundles.androidx)
implementation(libs.material)
implementation("androidx.core:core-ktx:1.12.0")
implementation("androidx.lifecycle:lifecycle-runtime-ktx:2.6.2")
implementation("androidx.activity:activity-compose:1.8.2")
implementation(platform("androidx.compose:compose-bom:2023.08.00"))
implementation("androidx.compose.ui:ui")
implementation("androidx.compose.ui:ui-graphics")
implementation("androidx.compose.ui:ui-tooling-preview")
implementation("androidx.compose.material3:material3")
implementation(project(":llama"))
testImplementation("junit:junit:4.13.2")
androidTestImplementation("androidx.test.ext:junit:1.1.5")
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
androidTestImplementation(platform("androidx.compose:compose-bom:2023.08.00"))
androidTestImplementation("androidx.compose.ui:ui-test-junit4")
debugImplementation("androidx.compose.ui:ui-tooling")
debugImplementation("androidx.compose.ui:ui-test-manifest")
implementation(project(":lib"))
testImplementation(libs.junit)
androidTestImplementation(libs.androidx.junit)
androidTestImplementation(libs.androidx.espresso.core)
}

8
examples/llama.android/app/proguard-rules.pro vendored
View File

@ -19,3 +19,11 @@
# If you keep the line number information, uncomment this to
# hide the original source file name.
#-renamesourcefileattribute SourceFile
-keep class com.arm.aichat.* { *; }
-keep class com.arm.aichat.gguf.* { *; }
-assumenosideeffects class android.util.Log {
public static int v(...);
public static int d(...);
}

13
examples/llama.android/app/src/main/AndroidManifest.xml
View File

@ -1,24 +1,21 @@
<?xml version="1.0" encoding="utf-8"?>
<manifest xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:tools="http://schemas.android.com/tools">
<uses-permission android:name="android.permission.INTERNET" />
<manifest xmlns:android="http://schemas.android.com/apk/res/android">
<application
android:allowBackup="true"
android:dataExtractionRules="@xml/data_extraction_rules"
android:extractNativeLibs="true"
android:fullBackupContent="@xml/backup_rules"
android:icon="@mipmap/ic_launcher"
android:icon="@mipmap/ic_launcher_round"
android:label="@string/app_name"
android:roundIcon="@mipmap/ic_launcher_round"
android:supportsRtl="true"
android:theme="@style/Theme.LlamaAndroid"
android:theme="@style/Theme.AiChatSample"
>
<activity
android:name=".MainActivity"
android:exported="true"
android:theme="@style/Theme.LlamaAndroid">
android:exported="true">
<intent-filter>
<action android:name="android.intent.action.MAIN" />

119
examples/llama.android/app/src/main/java/com/example/llama/Downloadable.kt
View File

@ -1,119 +0,0 @@
package com.example.llama
import android.app.DownloadManager
import android.net.Uri
import android.util.Log
import androidx.compose.material3.Button
import androidx.compose.material3.Text
import androidx.compose.runtime.Composable
import androidx.compose.runtime.getValue
import androidx.compose.runtime.mutableDoubleStateOf
import androidx.compose.runtime.mutableStateOf
import androidx.compose.runtime.remember
import androidx.compose.runtime.rememberCoroutineScope
import androidx.compose.runtime.setValue
import androidx.core.database.getLongOrNull
import androidx.core.net.toUri
import kotlinx.coroutines.delay
import kotlinx.coroutines.launch
import java.io.File
data class Downloadable(val name: String, val source: Uri, val destination: File) {
companion object {
@JvmStatic
private val tag: String? = this::class.qualifiedName
sealed interface State
data object Ready: State
data class Downloading(val id: Long): State
data class Downloaded(val downloadable: Downloadable): State
data class Error(val message: String): State
@JvmStatic
@Composable
fun Button(viewModel: MainViewModel, dm: DownloadManager, item: Downloadable) {
var status: State by remember {
mutableStateOf(
if (item.destination.exists()) Downloaded(item)
else Ready
)
}
var progress by remember { mutableDoubleStateOf(0.0) }
val coroutineScope = rememberCoroutineScope()
suspend fun waitForDownload(result: Downloading, item: Downloadable): State {
while (true) {
val cursor = dm.query(DownloadManager.Query().setFilterById(result.id))
if (cursor == null) {
Log.e(tag, "dm.query() returned null")
return Error("dm.query() returned null")
}
if (!cursor.moveToFirst() || cursor.count < 1) {
cursor.close()
Log.i(tag, "cursor.moveToFirst() returned false or cursor.count < 1, download canceled?")
return Ready
}
val pix = cursor.getColumnIndex(DownloadManager.COLUMN_BYTES_DOWNLOADED_SO_FAR)
val tix = cursor.getColumnIndex(DownloadManager.COLUMN_TOTAL_SIZE_BYTES)
val sofar = cursor.getLongOrNull(pix) ?: 0
val total = cursor.getLongOrNull(tix) ?: 1
cursor.close()
if (sofar == total) {
return Downloaded(item)
}
progress = (sofar * 1.0) / total
delay(1000L)
}
}
fun onClick() {
when (val s = status) {
is Downloaded -> {
viewModel.load(item.destination.path)
}
is Downloading -> {
coroutineScope.launch {
status = waitForDownload(s, item)
}
}
else -> {
item.destination.delete()
val request = DownloadManager.Request(item.source).apply {
setTitle("Downloading model")
setDescription("Downloading model: ${item.name}")
setAllowedNetworkTypes(DownloadManager.Request.NETWORK_WIFI)
setDestinationUri(item.destination.toUri())
}
viewModel.log("Saving ${item.name} to ${item.destination.path}")
Log.i(tag, "Saving ${item.name} to ${item.destination.path}")
val id = dm.enqueue(request)
status = Downloading(id)
onClick()
}
}
}
Button(onClick = { onClick() }, enabled = status !is Downloading) {
when (status) {
is Downloading -> Text(text = "Downloading ${(progress * 100).toInt()}%")
is Downloaded -> Text("Load ${item.name}")
is Ready -> Text("Download ${item.name}")
is Error -> Text("Download ${item.name}")
}
}
}
}
}

359
examples/llama.android/app/src/main/java/com/example/llama/MainActivity.kt
View File

@ -1,154 +1,257 @@
package com.example.llama
import android.app.ActivityManager
import android.app.DownloadManager
import android.content.ClipData
import android.content.ClipboardManager
import android.net.Uri
import android.os.Bundle
import android.os.StrictMode
import android.os.StrictMode.VmPolicy
import android.text.format.Formatter
import androidx.activity.ComponentActivity
import androidx.activity.compose.setContent
import androidx.activity.viewModels
import androidx.compose.foundation.layout.Box
import androidx.compose.foundation.layout.Column
import androidx.compose.foundation.layout.Row
import androidx.compose.foundation.layout.fillMaxSize
import androidx.compose.foundation.layout.padding
import androidx.compose.foundation.lazy.LazyColumn
import androidx.compose.foundation.lazy.items
import androidx.compose.foundation.lazy.rememberLazyListState
import androidx.compose.material3.Button
import androidx.compose.material3.LocalContentColor
import androidx.compose.material3.MaterialTheme
import androidx.compose.material3.OutlinedTextField
import androidx.compose.material3.Surface
import androidx.compose.material3.Text
import androidx.compose.runtime.Composable
import androidx.compose.ui.Modifier
import androidx.compose.ui.unit.dp
import androidx.core.content.getSystemService
import com.example.llama.ui.theme.LlamaAndroidTheme
import android.util.Log
import android.widget.EditText
import android.widget.TextView
import android.widget.Toast
import androidx.activity.enableEdgeToEdge
import androidx.activity.result.contract.ActivityResultContracts
import androidx.appcompat.app.AppCompatActivity
import androidx.lifecycle.lifecycleScope
import androidx.recyclerview.widget.LinearLayoutManager
import androidx.recyclerview.widget.RecyclerView
import com.arm.aichat.AiChat
import com.arm.aichat.InferenceEngine
import com.arm.aichat.gguf.GgufMetadata
import com.arm.aichat.gguf.GgufMetadataReader
import com.google.android.material.floatingactionbutton.FloatingActionButton
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.flow.onCompletion
import kotlinx.coroutines.launch
import kotlinx.coroutines.withContext
import java.io.File
import java.io.FileOutputStream
import java.io.InputStream
import java.util.UUID
class MainActivity(
activityManager: ActivityManager? = null,
downloadManager: DownloadManager? = null,
clipboardManager: ClipboardManager? = null,
): ComponentActivity() {
private val tag: String? = this::class.simpleName
class MainActivity : AppCompatActivity() {
private val activityManager by lazy { activityManager ?: getSystemService<ActivityManager>()!! }
private val downloadManager by lazy { downloadManager ?: getSystemService<DownloadManager>()!! }
private val clipboardManager by lazy { clipboardManager ?: getSystemService<ClipboardManager>()!! }
// Android views
private lateinit var ggufTv: TextView
private lateinit var messagesRv: RecyclerView
private lateinit var userInputEt: EditText
private lateinit var userActionFab: FloatingActionButton
private val viewModel: MainViewModel by viewModels()
// Arm AI Chat inference engine
private lateinit var engine: InferenceEngine
// Get a MemoryInfo object for the device's current memory status.
private fun availableMemory(): ActivityManager.MemoryInfo {
return ActivityManager.MemoryInfo().also { memoryInfo ->
activityManager.getMemoryInfo(memoryInfo)
}
}
// Conversation states
private var isModelReady = false
private val messages = mutableListOf<Message>()
private val lastAssistantMsg = StringBuilder()
private val messageAdapter = MessageAdapter(messages)
override fun onCreate(savedInstanceState: Bundle?) {
super.onCreate(savedInstanceState)
enableEdgeToEdge()
setContentView(R.layout.activity_main)
StrictMode.setVmPolicy(
VmPolicy.Builder(StrictMode.getVmPolicy())
.detectLeakedClosableObjects()
.build()
)
// Find views
ggufTv = findViewById(R.id.gguf)
messagesRv = findViewById(R.id.messages)
messagesRv.layoutManager = LinearLayoutManager(this)
messagesRv.adapter = messageAdapter
userInputEt = findViewById(R.id.user_input)
userActionFab = findViewById(R.id.fab)
val free = Formatter.formatFileSize(this, availableMemory().availMem)
val total = Formatter.formatFileSize(this, availableMemory().totalMem)
viewModel.log("Current memory: $free / $total")
viewModel.log("Downloads directory: ${getExternalFilesDir(null)}")
val extFilesDir = getExternalFilesDir(null)
val models = listOf(
Downloadable(
"Phi-2 7B (Q4_0, 1.6 GiB)",
Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf?download=true"),
File(extFilesDir, "phi-2-q4_0.gguf"),
),
Downloadable(
"TinyLlama 1.1B (f16, 2.2 GiB)",
Uri.parse("https://huggingface.co/ggml-org/models/resolve/main/tinyllama-1.1b/ggml-model-f16.gguf?download=true"),
File(extFilesDir, "tinyllama-1.1-f16.gguf"),
),
Downloadable(
"Phi 2 DPO (Q3_K_M, 1.48 GiB)",
Uri.parse("https://huggingface.co/TheBloke/phi-2-dpo-GGUF/resolve/main/phi-2-dpo.Q3_K_M.gguf?download=true"),
File(extFilesDir, "phi-2-dpo.Q3_K_M.gguf")
),
)
setContent {
LlamaAndroidTheme {
// A surface container using the 'background' color from the theme
Surface(
modifier = Modifier.fillMaxSize(),
color = MaterialTheme.colorScheme.background
) {
MainCompose(
viewModel,
clipboardManager,
downloadManager,
models,
)
}
// Arm AI Chat initialization
lifecycleScope.launch(Dispatchers.Default) {
engine = AiChat.getInferenceEngine(applicationContext)
}
// Upon CTA button tapped
userActionFab.setOnClickListener {
if (isModelReady) {
// If model is ready, validate input and send to engine
handleUserInput()
} else {
// Otherwise, prompt user to select a GGUF metadata on the device
getContent.launch(arrayOf("*/*"))
}
}
}
}
@Composable
fun MainCompose(
viewModel: MainViewModel,
clipboard: ClipboardManager,
dm: DownloadManager,
models: List<Downloadable>
) {
Column {
val scrollState = rememberLazyListState()
private val getContent = registerForActivityResult(
ActivityResultContracts.OpenDocument()
) { uri ->
Log.i(TAG, "Selected file uri:\n $uri")
uri?.let { handleSelectedModel(it) }
}
Box(modifier = Modifier.weight(1f)) {
LazyColumn(state = scrollState) {
items(viewModel.messages) {
Text(
it,
style = MaterialTheme.typography.bodyLarge.copy(color = LocalContentColor.current),
modifier = Modifier.padding(16.dp)
)
/**
* Handles the file Uri from [getContent] result
*/
private fun handleSelectedModel(uri: Uri) {
// Update UI states
userActionFab.isEnabled = false
userInputEt.hint = "Parsing GGUF..."
ggufTv.text = "Parsing metadata from selected file \n$uri"
lifecycleScope.launch(Dispatchers.IO) {
// Parse GGUF metadata
Log.i(TAG, "Parsing GGUF metadata...")
contentResolver.openInputStream(uri)?.use {
GgufMetadataReader.create().readStructuredMetadata(it)
}?.let { metadata ->
// Update UI to show GGUF metadata to user
Log.i(TAG, "GGUF parsed: \n$metadata")
withContext(Dispatchers.Main) {
ggufTv.text = metadata.toString()
}
}
}
OutlinedTextField(
value = viewModel.message,
onValueChange = { viewModel.updateMessage(it) },
label = { Text("Message") },
)
Row {
Button({ viewModel.send() }) { Text("Send") }
Button({ viewModel.bench(8, 4, 1) }) { Text("Bench") }
Button({ viewModel.clear() }) { Text("Clear") }
Button({
viewModel.messages.joinToString("\n").let {
clipboard.setPrimaryClip(ClipData.newPlainText("", it))
}
}) { Text("Copy") }
}
Column {
for (model in models) {
Downloadable.Button(viewModel, dm, model)
// Ensure the model file is available
val modelName = metadata.filename() + FILE_EXTENSION_GGUF
contentResolver.openInputStream(uri)?.use { input ->
ensureModelFile(modelName, input)
}?.let { modelFile ->
loadModel(modelName, modelFile)
withContext(Dispatchers.Main) {
isModelReady = true
userInputEt.hint = "Type and send a message!"
userInputEt.isEnabled = true
userActionFab.setImageResource(R.drawable.outline_send_24)
userActionFab.isEnabled = true
}
}
}
}
}
/**
* Prepare the model file within app's private storage
*/
private suspend fun ensureModelFile(modelName: String, input: InputStream) =
withContext(Dispatchers.IO) {
File(ensureModelsDirectory(), modelName).also { file ->
// Copy the file into local storage if not yet done
if (!file.exists()) {
Log.i(TAG, "Start copying file to $modelName")
withContext(Dispatchers.Main) {
userInputEt.hint = "Copying file..."
}
FileOutputStream(file).use { input.copyTo(it) }
Log.i(TAG, "Finished copying file to $modelName")
} else {
Log.i(TAG, "File already exists $modelName")
}
}
}
/**
* Load the model file from the app private storage
*/
private suspend fun loadModel(modelName: String, modelFile: File) =
withContext(Dispatchers.IO) {
Log.i(TAG, "Loading model $modelName")
withContext(Dispatchers.Main) {
userInputEt.hint = "Loading model..."
}
engine.loadModel(modelFile.path)
}
/**
* Validate and send the user message into [InferenceEngine]
*/
private fun handleUserInput() {
userInputEt.text.toString().also { userSsg ->
if (userSsg.isEmpty()) {
Toast.makeText(this, "Input message is empty!", Toast.LENGTH_SHORT).show()
} else {
userInputEt.text = null
userActionFab.isEnabled = false
// Update message states
messages.add(Message(UUID.randomUUID().toString(), userSsg, true))
lastAssistantMsg.clear()
messages.add(Message(UUID.randomUUID().toString(), lastAssistantMsg.toString(), false))
lifecycleScope.launch(Dispatchers.Default) {
engine.sendUserPrompt(userSsg)
.onCompletion {
withContext(Dispatchers.Main) {
userActionFab.isEnabled = true
}
}.collect { token ->
val messageCount = messages.size
check(messageCount > 0 && !messages[messageCount - 1].isUser)
messages.removeAt(messageCount - 1).copy(
content = lastAssistantMsg.append(token).toString()
).let { messages.add(it) }
withContext(Dispatchers.Main) {
messageAdapter.notifyItemChanged(messages.size - 1)
}
}
}
}
}
}
/**
* Run a benchmark with the model file
*/
private suspend fun runBenchmark(modelName: String, modelFile: File) =
withContext(Dispatchers.Default) {
Log.i(TAG, "Starts benchmarking $modelName")
withContext(Dispatchers.Main) {
userInputEt.hint = "Running benchmark..."
}
engine.bench(
pp=BENCH_PROMPT_PROCESSING_TOKENS,
tg=BENCH_TOKEN_GENERATION_TOKENS,
pl=BENCH_SEQUENCE,
nr=BENCH_REPETITION
).let { result ->
messages.add(Message(UUID.randomUUID().toString(), result, false))
withContext(Dispatchers.Main) {
messageAdapter.notifyItemChanged(messages.size - 1)
}
}
}
/**
* Create the `models` directory if not exist.
*/
private fun ensureModelsDirectory() =
File(filesDir, DIRECTORY_MODELS).also {
if (it.exists() && !it.isDirectory) { it.delete() }
if (!it.exists()) { it.mkdir() }
}
companion object {
private val TAG = MainActivity::class.java.simpleName
private const val DIRECTORY_MODELS = "models"
private const val FILE_EXTENSION_GGUF = ".gguf"
private const val BENCH_PROMPT_PROCESSING_TOKENS = 512
private const val BENCH_TOKEN_GENERATION_TOKENS = 128
private const val BENCH_SEQUENCE = 1
private const val BENCH_REPETITION = 3
}
}
fun GgufMetadata.filename() = when {
basic.name != null -> {
basic.name?.let { name ->
basic.sizeLabel?.let { size ->
"$name-$size"
} ?: name
}
}
architecture?.architecture != null -> {
architecture?.architecture?.let { arch ->
basic.uuid?.let { uuid ->
"$arch-$uuid"
} ?: "$arch-${System.currentTimeMillis()}"
}
}
else -> {
"model-${System.currentTimeMillis().toHexString()}"
}
}

105
examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt
View File

@ -1,105 +0,0 @@
package com.example.llama
import android.llama.cpp.LLamaAndroid
import android.util.Log
import androidx.compose.runtime.getValue
import androidx.compose.runtime.mutableStateOf
import androidx.compose.runtime.setValue
import androidx.lifecycle.ViewModel
import androidx.lifecycle.viewModelScope
import kotlinx.coroutines.flow.catch
import kotlinx.coroutines.launch
class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() {
companion object {
@JvmStatic
private val NanosPerSecond = 1_000_000_000.0
}
private val tag: String? = this::class.simpleName
var messages by mutableStateOf(listOf("Initializing..."))
private set
var message by mutableStateOf("")
private set
override fun onCleared() {
super.onCleared()
viewModelScope.launch {
try {
llamaAndroid.unload()
} catch (exc: IllegalStateException) {
messages += exc.message!!
}
}
}
fun send() {
val text = message
message = ""
// Add to messages console.
messages += text
messages += ""
viewModelScope.launch {
llamaAndroid.send(text)
.catch {
Log.e(tag, "send() failed", it)
messages += it.message!!
}
.collect { messages = messages.dropLast(1) + (messages.last() + it) }
}
}
fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1) {
viewModelScope.launch {
try {
val start = System.nanoTime()
val warmupResult = llamaAndroid.bench(pp, tg, pl, nr)
val end = System.nanoTime()
messages += warmupResult
val warmup = (end - start).toDouble() / NanosPerSecond
messages += "Warm up time: $warmup seconds, please wait..."
if (warmup > 5.0) {
messages += "Warm up took too long, aborting benchmark"
return@launch
}
messages += llamaAndroid.bench(512, 128, 1, 3)
} catch (exc: IllegalStateException) {
Log.e(tag, "bench() failed", exc)
messages += exc.message!!
}
}
}
fun load(pathToModel: String) {
viewModelScope.launch {
try {
llamaAndroid.load(pathToModel)
messages += "Loaded $pathToModel"
} catch (exc: IllegalStateException) {
Log.e(tag, "load() failed", exc)
messages += exc.message!!
}
}
}
fun updateMessage(newMessage: String) {
message = newMessage
}
fun clear() {
messages = listOf()
}
fun log(message: String) {
messages += message
}
}

51
examples/llama.android/app/src/main/java/com/example/llama/MessageAdapter.kt Normal file
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@ -0,0 +1,51 @@
package com.example.llama
import android.view.LayoutInflater
import android.view.View
import android.view.ViewGroup
import android.widget.TextView
import androidx.recyclerview.widget.RecyclerView
data class Message(
val id: String,
val content: String,
val isUser: Boolean
)
class MessageAdapter(
private val messages: List<Message>
) : RecyclerView.Adapter<RecyclerView.ViewHolder>() {
companion object {
private const val VIEW_TYPE_USER = 1
private const val VIEW_TYPE_ASSISTANT = 2
}
override fun getItemViewType(position: Int): Int {
return if (messages[position].isUser) VIEW_TYPE_USER else VIEW_TYPE_ASSISTANT
}
override fun onCreateViewHolder(parent: ViewGroup, viewType: Int): RecyclerView.ViewHolder {
val layoutInflater = LayoutInflater.from(parent.context)
return if (viewType == VIEW_TYPE_USER) {
val view = layoutInflater.inflate(R.layout.item_message_user, parent, false)
UserMessageViewHolder(view)
} else {
val view = layoutInflater.inflate(R.layout.item_message_assistant, parent, false)
AssistantMessageViewHolder(view)
}
}
override fun onBindViewHolder(holder: RecyclerView.ViewHolder, position: Int) {
val message = messages[position]
if (holder is UserMessageViewHolder || holder is AssistantMessageViewHolder) {
val textView = holder.itemView.findViewById<TextView>(R.id.msg_content)
textView.text = message.content
}
}
override fun getItemCount(): Int = messages.size
class UserMessageViewHolder(view: View) : RecyclerView.ViewHolder(view)
class AssistantMessageViewHolder(view: View) : RecyclerView.ViewHolder(view)
}

11
examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Color.kt
View File

@ -1,11 +0,0 @@
package com.example.llama.ui.theme
import androidx.compose.ui.graphics.Color
val Purple80 = Color(0xFFD0BCFF)
val PurpleGrey80 = Color(0xFFCCC2DC)
val Pink80 = Color(0xFFEFB8C8)
val Purple40 = Color(0xFF6650a4)
val PurpleGrey40 = Color(0xFF625b71)
val Pink40 = Color(0xFF7D5260)

70
examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Theme.kt
View File

@ -1,70 +0,0 @@
package com.example.llama.ui.theme
import android.app.Activity
import android.os.Build
import androidx.compose.foundation.isSystemInDarkTheme
import androidx.compose.material3.MaterialTheme
import androidx.compose.material3.darkColorScheme
import androidx.compose.material3.dynamicDarkColorScheme
import androidx.compose.material3.dynamicLightColorScheme
import androidx.compose.material3.lightColorScheme
import androidx.compose.runtime.Composable
import androidx.compose.runtime.SideEffect
import androidx.compose.ui.graphics.toArgb
import androidx.compose.ui.platform.LocalContext
import androidx.compose.ui.platform.LocalView
import androidx.core.view.WindowCompat
private val DarkColorScheme = darkColorScheme(
primary = Purple80,
secondary = PurpleGrey80,
tertiary = Pink80
)
private val LightColorScheme = lightColorScheme(
primary = Purple40,
secondary = PurpleGrey40,
tertiary = Pink40
/* Other default colors to override
background = Color(0xFFFFFBFE),
surface = Color(0xFFFFFBFE),
onPrimary = Color.White,
onSecondary = Color.White,
onTertiary = Color.White,
onBackground = Color(0xFF1C1B1F),
onSurface = Color(0xFF1C1B1F),
*/
)
@Composable
fun LlamaAndroidTheme(
darkTheme: Boolean = isSystemInDarkTheme(),
// Dynamic color is available on Android 12+
dynamicColor: Boolean = true,
content: @Composable () -> Unit
) {
val colorScheme = when {
dynamicColor && Build.VERSION.SDK_INT >= Build.VERSION_CODES.S -> {
val context = LocalContext.current
if (darkTheme) dynamicDarkColorScheme(context) else dynamicLightColorScheme(context)
}
darkTheme -> DarkColorScheme
else -> LightColorScheme
}
val view = LocalView.current
if (!view.isInEditMode) {
SideEffect {
val window = (view.context as Activity).window
window.statusBarColor = colorScheme.primary.toArgb()
WindowCompat.getInsetsController(window, view).isAppearanceLightStatusBars = darkTheme
}
}
MaterialTheme(
colorScheme = colorScheme,
typography = Typography,
content = content
)
}

34
examples/llama.android/app/src/main/java/com/example/llama/ui/theme/Type.kt
View File

@ -1,34 +0,0 @@
package com.example.llama.ui.theme
import androidx.compose.material3.Typography
import androidx.compose.ui.text.TextStyle
import androidx.compose.ui.text.font.FontFamily
import androidx.compose.ui.text.font.FontWeight
import androidx.compose.ui.unit.sp
// Set of Material typography styles to start with
val Typography = Typography(
bodyLarge = TextStyle(
fontFamily = FontFamily.Default,
fontWeight = FontWeight.Normal,
fontSize = 16.sp,
lineHeight = 24.sp,
letterSpacing = 0.5.sp
)
/* Other default text styles to override
titleLarge = TextStyle(
fontFamily = FontFamily.Default,
fontWeight = FontWeight.Normal,
fontSize = 22.sp,
lineHeight = 28.sp,
letterSpacing = 0.sp
),
labelSmall = TextStyle(
fontFamily = FontFamily.Default,
fontWeight = FontWeight.Medium,
fontSize = 11.sp,
lineHeight = 16.sp,
letterSpacing = 0.5.sp
)
*/
)

4
examples/llama.android/app/src/main/res/drawable/bg_assistant_message.xml Normal file
View File

@ -0,0 +1,4 @@
<shape xmlns:android="http://schemas.android.com/apk/res/android" android:shape="rectangle">
<solid android:color="#E5E5EA" />
<corners android:radius="16dp" />
</shape>

4
examples/llama.android/app/src/main/res/drawable/bg_user_message.xml Normal file
View File

@ -0,0 +1,4 @@
<shape xmlns:android="http://schemas.android.com/apk/res/android" android:shape="rectangle">
<solid android:color="#4285F4" />
<corners android:radius="16dp" />
</shape>

10
examples/llama.android/app/src/main/res/drawable/outline_folder_open_24.xml Normal file
View File

@ -0,0 +1,10 @@
<vector xmlns:android="http://schemas.android.com/apk/res/android"
android:width="24dp"
android:height="24dp"
android:viewportWidth="24"
android:viewportHeight="24"
android:tint="?attr/colorControlNormal">
<path
android:fillColor="@android:color/white"
android:pathData="M20,6h-8l-2,-2L4,4c-1.1,0 -1.99,0.9 -1.99,2L2,18c0,1.1 0.9,2 2,2h16c1.1,0 2,-0.9 2,-2L22,8c0,-1.1 -0.9,-2 -2,-2zM20,18L4,18L4,8h16v10z"/>
</vector>

11
examples/llama.android/app/src/main/res/drawable/outline_send_24.xml Normal file
View File

@ -0,0 +1,11 @@
<vector xmlns:android="http://schemas.android.com/apk/res/android"
android:width="24dp"
android:height="24dp"
android:viewportWidth="24"
android:viewportHeight="24"
android:tint="?attr/colorControlNormal"
android:autoMirrored="true">
<path
android:fillColor="@android:color/white"
android:pathData="M4.01,6.03l7.51,3.22 -7.52,-1 0.01,-2.22m7.5,8.72L4,17.97v-2.22l7.51,-1M2.01,3L2,10l15,2 -15,2 0.01,7L23,12 2.01,3z"/>
</vector>

78
examples/llama.android/app/src/main/res/layout/activity_main.xml Normal file
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@ -0,0 +1,78 @@
<?xml version="1.0" encoding="utf-8"?>
<androidx.constraintlayout.widget.ConstraintLayout xmlns:android="http://schemas.android.com/apk/res/android"
xmlns:app="http://schemas.android.com/apk/res-auto"
xmlns:tools="http://schemas.android.com/tools"
android:id="@+id/main"
android:layout_height="match_parent"
android:layout_width="match_parent">
<LinearLayout
android:fitsSystemWindows="true"
android:layout_width="match_parent"
android:layout_height="match_parent"
android:orientation="vertical"
android:layout_marginEnd="4dp"
tools:context=".MainActivity">
<ScrollView
android:layout_width="match_parent"
android:layout_height="0dp"
android:layout_weight="1"
android:fadeScrollbars="false">
<TextView
android:id="@+id/gguf"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_margin="16dp"
android:text="Selected GGUF model's metadata will show here."
style="@style/TextAppearance.MaterialComponents.Body2" />
</ScrollView>
<com.google.android.material.divider.MaterialDivider
android:layout_width="match_parent"
android:layout_height="2dp"
android:layout_marginHorizontal="16dp"
android:layout_marginVertical="8dp" />
<androidx.recyclerview.widget.RecyclerView
android:id="@+id/messages"
android:layout_width="match_parent"
android:layout_height="0dp"
android:layout_weight="4"
android:fadeScrollbars="false"
android:scrollbars="vertical"
app:reverseLayout="true"
tools:listitem="@layout/item_message_assistant"/>
<LinearLayout
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:orientation="horizontal"
android:paddingStart="16dp"
android:paddingEnd="4dp">
<EditText
android:id="@+id/user_input"
android:enabled="false"
android:layout_width="0dp"
android:layout_weight="1"
android:layout_height="match_parent"
android:padding="8dp"
style="@style/TextAppearance.MaterialComponents.Body2"
android:hint="Please first pick a GGUF model file to import." />
<com.google.android.material.floatingactionbutton.FloatingActionButton
android:id="@+id/fab"
android:enabled="true"
style="@style/Widget.Material3.FloatingActionButton.Primary"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:layout_margin="12dp"
android:src="@drawable/outline_folder_open_24" />
</LinearLayout>
</LinearLayout>
</androidx.constraintlayout.widget.ConstraintLayout>

16
examples/llama.android/app/src/main/res/layout/item_message_assistant.xml Normal file
View File

@ -0,0 +1,16 @@
<?xml version="1.0" encoding="utf-8"?>
<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_marginHorizontal="16dp"
android:layout_marginVertical="8dp"
android:gravity="start">
<TextView
android:id="@+id/msg_content"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:background="@drawable/bg_assistant_message"
android:padding="12dp"
android:textColor="@android:color/black" />
</LinearLayout>

16
examples/llama.android/app/src/main/res/layout/item_message_user.xml Normal file
View File

@ -0,0 +1,16 @@
<?xml version="1.0" encoding="utf-8"?>
<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
android:layout_width="match_parent"
android:layout_height="wrap_content"
android:layout_marginHorizontal="16dp"
android:layout_marginVertical="8dp"
android:gravity="end">
<TextView
android:id="@+id/msg_content"
android:layout_width="wrap_content"
android:layout_height="wrap_content"
android:background="@drawable/bg_user_message"
android:padding="12dp"
android:textColor="@android:color/white" />
</LinearLayout>

2
examples/llama.android/app/src/main/res/values/strings.xml
View File

@ -1,3 +1,3 @@
<resources>
<string name="app_name">LlamaAndroid</string>
<string name="app_name">AI Chat basic sample</string>
</resources>

7
examples/llama.android/app/src/main/res/values/themes.xml
View File

@ -1,5 +1,10 @@
<?xml version="1.0" encoding="utf-8"?>
<resources>
<style name="Theme.LlamaAndroid" parent="android:Theme.Material.Light.NoActionBar" />
<style name="Base.Theme.AiChatSample" parent="Theme.Material3.DayNight.NoActionBar">
<!-- Customize your light theme here. -->
<!-- <item name="colorPrimary">@color/my_light_primary</item> -->
</style>
<style name="Theme.AiChatSample" parent="Base.Theme.AiChatSample" />
</resources>

6
examples/llama.android/build.gradle.kts
View File

@ -1,6 +1,6 @@
// Top-level build file where you can add configuration options common to all sub-projects/modules.
plugins {
id("com.android.application") version "8.2.0" apply false
id("org.jetbrains.kotlin.android") version "1.9.0" apply false
id("com.android.library") version "8.2.0" apply false
alias(libs.plugins.android.application) apply false
alias(libs.plugins.android.library) apply false
alias(libs.plugins.jetbrains.kotlin.android) apply false
}

1
examples/llama.android/gradle.properties
View File

@ -21,3 +21,4 @@ kotlin.code.style=official
# resources declared in the library itself and none from the library's dependencies,
# thereby reducing the size of the R class for that library
android.nonTransitiveRClass=true
android.native.buildOutput=verbose

53
examples/llama.android/gradle/libs.versions.toml Normal file
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@ -0,0 +1,53 @@
[versions]
# Plugins
agp = "8.13.0"
kotlin = "2.2.20"
# AndroidX
activity = "1.11.0"
appcompat = "1.7.1"
core-ktx = "1.17.0"
constraint-layout = "2.2.1"
datastore-preferences = "1.1.7"
# Material
material = "1.13.0"
# Testing
espresso-core = "3.7.0"
androidx-junit = "1.3.0"
junit = "4.13.2"
[plugins]
android-application = { id = "com.android.application", version.ref = "agp" }
android-library = { id = "com.android.library", version.ref = "agp" }
jetbrains-kotlin-android = { id = "org.jetbrains.kotlin.android", version.ref = "kotlin" }
[libraries]
# AndroidX
androidx-activity = { group = "androidx.activity", name = "activity", version.ref = "activity" }
androidx-appcompat = { group = "androidx.appcompat", name = "appcompat", version.ref = "appcompat" }
androidx-constraintlayout = { group = "androidx.constraintlayout", name = "constraintlayout", version.ref = "constraint-layout" }
androidx-core-ktx = { group = "androidx.core", name = "core-ktx", version.ref = "core-ktx" }
androidx-datastore-preferences = { group = "androidx.datastore", name = "datastore-preferences", version.ref = "datastore-preferences" }
#Material
material = { group = "com.google.android.material", name = "material", version.ref = "material" }
# Testing
androidx-espresso-core = { group = "androidx.test.espresso", name = "espresso-core", version.ref = "espresso-core" }
androidx-junit = { group = "androidx.test.ext", name = "junit", version.ref = "androidx-junit" }
junit = { group = "junit", name = "junit", version.ref = "junit" }
[bundles]
androidx = [
"androidx-activity",
"androidx-appcompat",
"androidx-constraintlayout",
"androidx-core-ktx",
"androidx-datastore-preferences",
]

4
examples/llama.android/gradle/wrapper/gradle-wrapper.properties vendored
View File

@ -1,6 +1,6 @@
#Thu Dec 21 14:31:09 AEDT 2023
#Tue Apr 01 11:15:06 PDT 2025
distributionBase=GRADLE_USER_HOME
distributionPath=wrapper/dists
distributionUrl=https\://services.gradle.org/distributions/gradle-8.2-bin.zip
distributionUrl=https\://services.gradle.org/distributions/gradle-8.14.3-bin.zip
zipStoreBase=GRADLE_USER_HOME
zipStorePath=wrapper/dists

0
examples/llama.android/llama/.gitignore → examples/llama.android/lib/.gitignore vendored
View File

78
examples/llama.android/lib/build.gradle.kts Normal file
View File

@ -0,0 +1,78 @@
plugins {
alias(libs.plugins.android.library)
alias(libs.plugins.jetbrains.kotlin.android)
}
android {
namespace = "com.arm.aichat"
compileSdk = 36
ndkVersion = "29.0.13113456"
defaultConfig {
minSdk = 33
testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
consumerProguardFiles("consumer-rules.pro")
ndk {
abiFilters += listOf("arm64-v8a", "x86_64")
}
externalNativeBuild {
cmake {
arguments += "-DCMAKE_BUILD_TYPE=Release"
arguments += "-DCMAKE_MESSAGE_LOG_LEVEL=DEBUG"
arguments += "-DCMAKE_VERBOSE_MAKEFILE=ON"
arguments += "-DBUILD_SHARED_LIBS=ON"
arguments += "-DLLAMA_BUILD_COMMON=ON"
arguments += "-DLLAMA_CURL=OFF"
arguments += "-DGGML_NATIVE=OFF"
arguments += "-DGGML_BACKEND_DL=ON"
arguments += "-DGGML_CPU_ALL_VARIANTS=ON"
arguments += "-DGGML_LLAMAFILE=OFF"
}
}
aarMetadata {
minCompileSdk = 35
}
}
externalNativeBuild {
cmake {
path("src/main/cpp/CMakeLists.txt")
version = "3.31.6"
}
}
compileOptions {
sourceCompatibility = JavaVersion.VERSION_17
targetCompatibility = JavaVersion.VERSION_17
}
kotlin {
jvmToolchain(17)
compileOptions {
targetCompatibility = JavaVersion.VERSION_17
}
}
packaging {
resources {
excludes += "/META-INF/{AL2.0,LGPL2.1}"
}
}
publishing {
singleVariant("release") {
withJavadocJar()
}
}
}
dependencies {
implementation(libs.androidx.core.ktx)
implementation(libs.androidx.datastore.preferences)
testImplementation(libs.junit)
androidTestImplementation(libs.androidx.junit)
}

8
examples/llama.android/lib/consumer-rules.pro Normal file
View File

@ -0,0 +1,8 @@
-keep class com.arm.aichat.* { *; }
-keep class com.arm.aichat.gguf.* { *; }
-keepclasseswithmembernames class * {
native <methods>;
}
-keep class kotlin.Metadata { *; }

0
examples/llama.android/llama/proguard-rules.pro → examples/llama.android/lib/proguard-rules.pro vendored
View File

0
examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt → examples/llama.android/lib/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt
View File

0
examples/llama.android/llama/src/main/AndroidManifest.xml → examples/llama.android/lib/src/main/AndroidManifest.xml
View File

56
examples/llama.android/lib/src/main/cpp/CMakeLists.txt Normal file
View File

@ -0,0 +1,56 @@
cmake_minimum_required(VERSION 3.31.6)
project("ai-chat" VERSION 1.0.0 LANGUAGES C CXX)
set(CMAKE_C_STANDARD 11)
set(CMAKE_C_STANDARD_REQUIRED true)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_STANDARD_REQUIRED true)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}" CACHE STRING "" FORCE)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}" CACHE STRING "" FORCE)
# --------------------------------------------------------------------------
# AI Chat library
# --------------------------------------------------------------------------
if(DEFINED ANDROID_ABI)
message(STATUS "Detected Android ABI: ${ANDROID_ABI}")
if(ANDROID_ABI STREQUAL "arm64-v8a")
set(GGML_SYSTEM_ARCH "ARM")
set(GGML_CPU_KLEIDIAI ON)
set(GGML_OPENMP ON)
elseif(ANDROID_ABI STREQUAL "x86_64")
set(GGML_SYSTEM_ARCH "x86")
set(GGML_CPU_KLEIDIAI OFF)
set(GGML_OPENMP OFF)
else()
message(FATAL_ERROR "Unsupported ABI: ${ANDROID_ABI}")
endif()
endif()
set(LLAMA_SRC ${CMAKE_CURRENT_LIST_DIR}/../../../../../../)
add_subdirectory(${LLAMA_SRC} build-llama)
add_library(${CMAKE_PROJECT_NAME} SHARED
ai_chat.cpp)
target_compile_definitions(${CMAKE_PROJECT_NAME} PRIVATE
GGML_SYSTEM_ARCH=${GGML_SYSTEM_ARCH}
GGML_CPU_KLEIDIAI=$<BOOL:${GGML_CPU_KLEIDIAI}>
GGML_OPENMP=$<BOOL:${GGML_OPENMP}>
)
target_include_directories(${CMAKE_PROJECT_NAME} PRIVATE
${LLAMA_SRC}
${LLAMA_SRC}/common
${LLAMA_SRC}/include
${LLAMA_SRC}/ggml/include
${LLAMA_SRC}/ggml/src)
target_link_libraries(${CMAKE_PROJECT_NAME}
llama
common
android
log)

565
examples/llama.android/lib/src/main/cpp/ai_chat.cpp Normal file
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@ -0,0 +1,565 @@
#include <android/log.h>
#include <jni.h>
#include <iomanip>
#include <cmath>
#include <string>
#include <unistd.h>
#include <sampling.h>
#include "logging.h"
#include "chat.h"
#include "common.h"
#include "llama.h"
template<class T>
static std::string join(const std::vector<T> &values, const std::string &delim) {
std::ostringstream str;
for (size_t i = 0; i < values.size(); i++) {
str << values[i];
if (i < values.size() - 1) { str << delim; }
}
return str.str();
}
/**
* LLama resources: context, model, batch and sampler
*/
constexpr int N_THREADS_MIN = 2;
constexpr int N_THREADS_MAX = 4;
constexpr int N_THREADS_HEADROOM = 2;
constexpr int DEFAULT_CONTEXT_SIZE = 8192;
constexpr int OVERFLOW_HEADROOM = 4;
constexpr int BATCH_SIZE = 512;
constexpr float DEFAULT_SAMPLER_TEMP = 0.3f;
static llama_model * g_model;
static llama_context * g_context;
static llama_batch g_batch;
static common_chat_templates_ptr g_chat_templates;
static common_sampler * g_sampler;
extern "C"
JNIEXPORT void JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_init(JNIEnv *env, jobject /*unused*/, jstring nativeLibDir) {
// Set llama log handler to Android
llama_log_set(aichat_android_log_callback, nullptr);
// Loading all CPU backend variants
const auto *path_to_backend = env->GetStringUTFChars(nativeLibDir, 0);
LOGi("Loading backends from %s", path_to_backend);
ggml_backend_load_all_from_path(path_to_backend);
env->ReleaseStringUTFChars(nativeLibDir, path_to_backend);
// Initialize backends
llama_backend_init();
LOGi("Backend initiated; Log handler set.");
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_load(JNIEnv *env, jobject, jstring jmodel_path) {
llama_model_params model_params = llama_model_default_params();
const auto *model_path = env->GetStringUTFChars(jmodel_path, 0);
LOGd("%s: Loading model from: \n%s\n", __func__, model_path);
auto *model = llama_model_load_from_file(model_path, model_params);
env->ReleaseStringUTFChars(jmodel_path, model_path);
if (!model) {
return 1;
}
g_model = model;
return 0;
}
static llama_context *init_context(llama_model *model, const int n_ctx = DEFAULT_CONTEXT_SIZE) {
if (!model) {
LOGe("%s: model cannot be null", __func__);
return nullptr;
}
// Multi-threading setup
const int n_threads = std::max(N_THREADS_MIN, std::min(N_THREADS_MAX,
(int) sysconf(_SC_NPROCESSORS_ONLN) -
N_THREADS_HEADROOM));
LOGi("%s: Using %d threads", __func__, n_threads);
// Context parameters setup
llama_context_params ctx_params = llama_context_default_params();
const int trained_context_size = llama_model_n_ctx_train(model);
if (n_ctx > trained_context_size) {
LOGw("%s: Model was trained with only %d context size! Enforcing %d context size...",
__func__, trained_context_size, n_ctx);
}
ctx_params.n_ctx = n_ctx;
ctx_params.n_batch = BATCH_SIZE;
ctx_params.n_ubatch = BATCH_SIZE;
ctx_params.n_threads = n_threads;
ctx_params.n_threads_batch = n_threads;
auto *context = llama_init_from_model(g_model, ctx_params);
if (context == nullptr) {
LOGe("%s: llama_new_context_with_model() returned null)", __func__);
}
return context;
}
static common_sampler *new_sampler(float temp) {
common_params_sampling sparams;
sparams.temp = temp;
return common_sampler_init(g_model, sparams);
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_prepare(JNIEnv * /*env*/, jobject /*unused*/) {
auto *context = init_context(g_model);
if (!context) { return 1; }
g_context = context;
g_batch = llama_batch_init(BATCH_SIZE, 0, 1);
g_chat_templates = common_chat_templates_init(g_model, "");
g_sampler = new_sampler(DEFAULT_SAMPLER_TEMP);
return 0;
}
static std::string get_backend() {
std::vector<std::string> backends;
for (size_t i = 0; i < ggml_backend_reg_count(); i++) {
auto *reg = ggml_backend_reg_get(i);
std::string name = ggml_backend_reg_name(reg);
if (name != "CPU") {
backends.push_back(ggml_backend_reg_name(reg));
}
}
return backends.empty() ? "CPU" : join(backends, ",");
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_systemInfo(JNIEnv *env, jobject /*unused*/) {
return env->NewStringUTF(llama_print_system_info());
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_benchModel(JNIEnv *env, jobject /*unused*/, jint pp, jint tg,
jint pl, jint nr) {
auto *context = init_context(g_model, pp);
if (!context) {
const auto *const err_msg = "Fail to init_context! Bench aborted.";
LOGe(err_msg);
return env->NewStringUTF(err_msg);
}
auto pp_avg = 0.0;
auto tg_avg = 0.0;
auto pp_std = 0.0;
auto tg_std = 0.0;
const uint32_t n_ctx = llama_n_ctx(context);
LOGi("n_ctx = %d", n_ctx);
int i, j;
int nri;
for (nri = 0; nri < nr; nri++) {
LOGi("Benchmark prompt processing (pp = %d)", pp);
common_batch_clear(g_batch);
const int n_tokens = pp;
for (i = 0; i < n_tokens; i++) {
common_batch_add(g_batch, 0, i, {0}, false);
}
g_batch.logits[g_batch.n_tokens - 1] = true;
llama_memory_clear(llama_get_memory(context), false);
const auto t_pp_start = ggml_time_us();
if (llama_decode(context, g_batch) != 0) {
LOGe("llama_decode() failed during prompt processing");
}
const auto t_pp_end = ggml_time_us();
// bench text generation
LOGi("Benchmark text generation (tg = %d)", tg);
llama_memory_clear(llama_get_memory(context), false);
const auto t_tg_start = ggml_time_us();
for (i = 0; i < tg; i++) {
common_batch_clear(g_batch);
for (j = 0; j < pl; j++) {
common_batch_add(g_batch, 0, i, {j}, true);
}
if (llama_decode(context, g_batch) != 0) {
LOGe("llama_decode() failed during text generation");
}
}
const auto t_tg_end = ggml_time_us();
llama_memory_clear(llama_get_memory(context), false);
const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
const auto speed_pp = double(pp) / t_pp;
const auto speed_tg = double(pl * tg) / t_tg;
pp_avg += speed_pp;
tg_avg += speed_tg;
pp_std += speed_pp * speed_pp;
tg_std += speed_tg * speed_tg;
LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg);
}
llama_free(context);
pp_avg /= double(nr);
tg_avg /= double(nr);
if (nr > 1) {
pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1));
tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1));
} else {
pp_std = 0;
tg_std = 0;
}
char model_desc[128];
llama_model_desc(g_model, model_desc, sizeof(model_desc));
const auto model_size = double(llama_model_size(g_model)) / 1024.0 / 1024.0 / 1024.0;
const auto model_n_params = double(llama_model_n_params(g_model)) / 1e9;
const auto backend = get_backend();
std::stringstream result;
result << std::setprecision(3);
result << "| model | size | params | backend | test | t/s |\n";
result << "| --- | --- | --- | --- | --- | --- |\n";
result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | "
<< backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n";
result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | "
<< backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n";
return env->NewStringUTF(result.str().c_str());
}
/**
* Completion loop's long-term states:
* - chat management
* - position tracking
*/
constexpr const char *ROLE_SYSTEM = "system";
constexpr const char *ROLE_USER = "user";
constexpr const char *ROLE_ASSISTANT = "assistant";
static std::vector<common_chat_msg> chat_msgs;
static llama_pos system_prompt_position;
static llama_pos current_position;
static void reset_long_term_states(const bool clear_kv_cache = true) {
chat_msgs.clear();
system_prompt_position = 0;
current_position = 0;
if (clear_kv_cache)
llama_memory_clear(llama_get_memory(g_context), false);
}
/**
* TODO-hyin: implement sliding-window version as a better alternative
*
* Context shifting by discarding the older half of the tokens appended after system prompt:
* - take the [system_prompt_position] first tokens from the original prompt
* - take half of the last (system_prompt_position - system_prompt_position) tokens
* - recompute the logits in batches
*/
static void shift_context() {
const int n_discard = (current_position - system_prompt_position) / 2;
LOGi("%s: Discarding %d tokens", __func__, n_discard);
llama_memory_seq_rm(llama_get_memory(g_context), 0, system_prompt_position, system_prompt_position + n_discard);
llama_memory_seq_add(llama_get_memory(g_context), 0, system_prompt_position + n_discard, current_position, -n_discard);
current_position -= n_discard;
LOGi("%s: Context shifting done! Current position: %d", __func__, current_position);
}
static std::string chat_add_and_format(const std::string &role, const std::string &content) {
common_chat_msg new_msg;
new_msg.role = role;
new_msg.content = content;
auto formatted = common_chat_format_single(
g_chat_templates.get(), chat_msgs, new_msg, role == ROLE_USER, /* use_jinja */ false);
chat_msgs.push_back(new_msg);
LOGi("%s: Formatted and added %s message: \n%s\n", __func__, role.c_str(), formatted.c_str());
return formatted;
}
/**
* Completion loop's short-term states:
* - stop generation position
* - token chars caching
* - current assistant message being generated
*/
static llama_pos stop_generation_position;
static std::string cached_token_chars;
static std::ostringstream assistant_ss;
static void reset_short_term_states() {
stop_generation_position = 0;
cached_token_chars.clear();
assistant_ss.str("");
}
static int decode_tokens_in_batches(
llama_context *context,
llama_batch &batch,
const llama_tokens &tokens,
const llama_pos start_pos,
const bool compute_last_logit = false) {
// Process tokens in batches using the global batch
LOGd("%s: Decode %d tokens starting at position %d", __func__, (int) tokens.size(), start_pos);
for (int i = 0; i < (int) tokens.size(); i += BATCH_SIZE) {
const int cur_batch_size = std::min((int) tokens.size() - i, BATCH_SIZE);
common_batch_clear(batch);
LOGv("%s: Preparing a batch size of %d starting at: %d", __func__, cur_batch_size, i);
// Shift context if current batch cannot fit into the context
if (start_pos + i + cur_batch_size >= DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM) {
LOGw("%s: Current batch won't fit into context! Shifting...", __func__);
shift_context();
}
// Add tokens to the batch with proper positions
for (int j = 0; j < cur_batch_size; j++) {
const llama_token token_id = tokens[i + j];
const llama_pos position = start_pos + i + j;
const bool want_logit = compute_last_logit && (i + j == tokens.size() - 1);
common_batch_add(batch, token_id, position, {0}, want_logit);
}
// Decode this batch
const int decode_result = llama_decode(context, batch);
if (decode_result) {
LOGe("%s: llama_decode failed w/ %d", __func__, decode_result);
return 1;
}
}
return 0;
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_processSystemPrompt(
JNIEnv *env,
jobject /*unused*/,
jstring jsystem_prompt
) {
// Reset long-term & short-term states
reset_long_term_states();
reset_short_term_states();
// Obtain system prompt from JEnv
const auto *system_prompt = env->GetStringUTFChars(jsystem_prompt, nullptr);
LOGd("%s: System prompt received: \n%s", __func__, system_prompt);
std::string formatted_system_prompt(system_prompt);
env->ReleaseStringUTFChars(jsystem_prompt, system_prompt);
// Format system prompt if applicable
const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
if (has_chat_template) {
formatted_system_prompt = chat_add_and_format(ROLE_SYSTEM, system_prompt);
}
// Tokenize system prompt
const auto system_tokens = common_tokenize(g_context, formatted_system_prompt,
has_chat_template, has_chat_template);
for (auto id: system_tokens) {
LOGv("token: `%s`\t -> `%d`", common_token_to_piece(g_context, id).c_str(), id);
}
// Handle context overflow
const int max_batch_size = DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM;
if ((int) system_tokens.size() > max_batch_size) {
LOGe("%s: System prompt too long for context! %d tokens, max: %d",
__func__, (int) system_tokens.size(), max_batch_size);
return 1;
}
// Decode system tokens in batches
if (decode_tokens_in_batches(g_context, g_batch, system_tokens, current_position)) {
LOGe("%s: llama_decode() failed!", __func__);
return 2;
}
// Update position
system_prompt_position = current_position = (int) system_tokens.size();
return 0;
}
extern "C"
JNIEXPORT jint JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_processUserPrompt(
JNIEnv *env,
jobject /*unused*/,
jstring juser_prompt,
jint n_predict
) {
// Reset short-term states
reset_short_term_states();
// Obtain and tokenize user prompt
const auto *const user_prompt = env->GetStringUTFChars(juser_prompt, nullptr);
LOGd("%s: User prompt received: \n%s", __func__, user_prompt);
std::string formatted_user_prompt(user_prompt);
env->ReleaseStringUTFChars(juser_prompt, user_prompt);
// Format user prompt if applicable
const bool has_chat_template = common_chat_templates_was_explicit(g_chat_templates.get());
if (has_chat_template) {
formatted_user_prompt = chat_add_and_format(ROLE_USER, user_prompt);
}
// Decode formatted user prompts
auto user_tokens = common_tokenize(g_context, formatted_user_prompt, has_chat_template, has_chat_template);
for (auto id: user_tokens) {
LOGv("token: `%s`\t -> `%d`", common_token_to_piece(g_context, id).c_str(), id);
}
// Ensure user prompt doesn't exceed the context size by truncating if necessary.
const int user_prompt_size = (int) user_tokens.size();
const int max_batch_size = DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM;
if (user_prompt_size > max_batch_size) {
const int skipped_tokens = user_prompt_size - max_batch_size;
user_tokens.resize(max_batch_size);
LOGw("%s: User prompt too long! Skipped %d tokens!", __func__, skipped_tokens);
}
// Decode user tokens in batches
if (decode_tokens_in_batches(g_context, g_batch, user_tokens, current_position, true)) {
LOGe("%s: llama_decode() failed!", __func__);
return 2;
}
// Update position
current_position += user_prompt_size;
stop_generation_position = current_position + user_prompt_size + n_predict;
return 0;
}
static bool is_valid_utf8(const char *string) {
if (!string) { return true; }
const auto *bytes = (const unsigned char *) string;
int num;
while (*bytes != 0x00) {
if ((*bytes & 0x80) == 0x00) {
// U+0000 to U+007F
num = 1;
} else if ((*bytes & 0xE0) == 0xC0) {
// U+0080 to U+07FF
num = 2;
} else if ((*bytes & 0xF0) == 0xE0) {
// U+0800 to U+FFFF
num = 3;
} else if ((*bytes & 0xF8) == 0xF0) {
// U+10000 to U+10FFFF
num = 4;
} else {
return false;
}
bytes += 1;
for (int i = 1; i < num; ++i) {
if ((*bytes & 0xC0) != 0x80) {
return false;
}
bytes += 1;
}
}
return true;
}
extern "C"
JNIEXPORT jstring JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_generateNextToken(
JNIEnv *env,
jobject /*unused*/
) {
// Infinite text generation via context shifting
if (current_position >= DEFAULT_CONTEXT_SIZE - OVERFLOW_HEADROOM) {
LOGw("%s: Context full! Shifting...", __func__);
shift_context();
}
// Stop if reaching the marked position
if (current_position >= stop_generation_position) {
LOGw("%s: STOP: hitting stop position: %d", __func__, stop_generation_position);
return nullptr;
}
// Sample next token
const auto new_token_id = common_sampler_sample(g_sampler, g_context, -1);
common_sampler_accept(g_sampler, new_token_id, true);
// Populate the batch with new token, then decode
common_batch_clear(g_batch);
common_batch_add(g_batch, new_token_id, current_position, {0}, true);
if (llama_decode(g_context, g_batch) != 0) {
LOGe("%s: llama_decode() failed for generated token", __func__);
return nullptr;
}
// Update position
current_position++;
// Stop if next token is EOG
if (llama_vocab_is_eog(llama_model_get_vocab(g_model), new_token_id)) {
LOGd("id: %d,\tIS EOG!\nSTOP.", new_token_id);
chat_add_and_format(ROLE_ASSISTANT, assistant_ss.str());
return nullptr;
}
// If not EOG, convert to text
auto new_token_chars = common_token_to_piece(g_context, new_token_id);
cached_token_chars += new_token_chars;
// Create and return a valid UTF-8 Java string
jstring result = nullptr;
if (is_valid_utf8(cached_token_chars.c_str())) {
result = env->NewStringUTF(cached_token_chars.c_str());
LOGv("id: %d,\tcached: `%s`,\tnew: `%s`", new_token_id, cached_token_chars.c_str(), new_token_chars.c_str());
assistant_ss << cached_token_chars;
cached_token_chars.clear();
} else {
LOGv("id: %d,\tappend to cache", new_token_id);
result = env->NewStringUTF("");
}
return result;
}
extern "C"
JNIEXPORT void JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_unload(JNIEnv * /*unused*/, jobject /*unused*/) {
// Reset long-term & short-term states
reset_long_term_states();
reset_short_term_states();
// Free up resources
common_sampler_free(g_sampler);
g_chat_templates.reset();
llama_batch_free(g_batch);
llama_free(g_context);
llama_model_free(g_model);
}
extern "C"
JNIEXPORT void JNICALL
Java_com_arm_aichat_internal_InferenceEngineImpl_shutdown(JNIEnv *env, jobject /*unused*/) {
llama_backend_free();
}

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//
// Created by Han Yin on 10/31/25.
//
#ifndef AICHAT_LOGGING_H
#define AICHAT_LOGGING_H
#endif //AICHAT_LOGGING_H
#pragma once
#include <android/log.h>
#ifndef LOG_TAG
#define LOG_TAG "ai-chat"
#endif
#ifndef LOG_MIN_LEVEL
#if defined(NDEBUG)
#define LOG_MIN_LEVEL ANDROID_LOG_INFO
#else
#define LOG_MIN_LEVEL ANDROID_LOG_VERBOSE
#endif
#endif
static inline int ai_should_log(int prio) {
return __android_log_is_loggable(prio, LOG_TAG, LOG_MIN_LEVEL);
}
#if LOG_MIN_LEVEL <= ANDROID_LOG_VERBOSE
#define LOGv(...) do { if (ai_should_log(ANDROID_LOG_VERBOSE)) __android_log_print(ANDROID_LOG_VERBOSE, LOG_TAG, __VA_ARGS__); } while (0)
#else
#define LOGv(...) ((void)0)
#endif
#if LOG_MIN_LEVEL <= ANDROID_LOG_DEBUG
#define LOGd(...) do { if (ai_should_log(ANDROID_LOG_DEBUG)) __android_log_print(ANDROID_LOG_DEBUG, LOG_TAG, __VA_ARGS__); } while (0)
#else
#define LOGd(...) ((void)0)
#endif
#define LOGi(...) do { if (ai_should_log(ANDROID_LOG_INFO )) __android_log_print(ANDROID_LOG_INFO , LOG_TAG, __VA_ARGS__); } while (0)
#define LOGw(...) do { if (ai_should_log(ANDROID_LOG_WARN )) __android_log_print(ANDROID_LOG_WARN , LOG_TAG, __VA_ARGS__); } while (0)
#define LOGe(...) do { if (ai_should_log(ANDROID_LOG_ERROR)) __android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__); } while (0)
static inline int android_log_prio_from_ggml(enum ggml_log_level level) {
switch (level) {
case GGML_LOG_LEVEL_ERROR: return ANDROID_LOG_ERROR;
case GGML_LOG_LEVEL_WARN: return ANDROID_LOG_WARN;
case GGML_LOG_LEVEL_INFO: return ANDROID_LOG_INFO;
case GGML_LOG_LEVEL_DEBUG: return ANDROID_LOG_DEBUG;
default: return ANDROID_LOG_DEFAULT;
}
}
static inline void aichat_android_log_callback(enum ggml_log_level level,
const char* text,
void* /*user*/) {
const int prio = android_log_prio_from_ggml(level);
if (!ai_should_log(prio)) return;
__android_log_write(prio, LOG_TAG, text);
}

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examples/llama.android/lib/src/main/java/com/arm/aichat/AiChat.kt Normal file
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package com.arm.aichat
import android.content.Context
import com.arm.aichat.internal.InferenceEngineImpl
/**
* Main entry point for Arm's AI Chat library.
*/
object AiChat {
/**
* Get the inference engine single instance.
*/
fun getInferenceEngine(context: Context) = InferenceEngineImpl.getInstance(context)
}

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package com.arm.aichat
import com.arm.aichat.InferenceEngine.State
import kotlinx.coroutines.flow.Flow
import kotlinx.coroutines.flow.StateFlow
/**
* Interface defining the core LLM inference operations.
*/
interface InferenceEngine {
/**
* Current state of the inference engine
*/
val state: StateFlow<State>
/**
* Load a model from the given path.
*
* @throws UnsupportedArchitectureException if model architecture not supported
*/
suspend fun loadModel(pathToModel: String)
/**
* Sends a system prompt to the loaded model
*/
suspend fun setSystemPrompt(systemPrompt: String)
/**
* Sends a user prompt to the loaded model and returns a Flow of generated tokens.
*/
fun sendUserPrompt(message: String, predictLength: Int = DEFAULT_PREDICT_LENGTH): Flow<String>
/**
* Runs a benchmark with the specified parameters.
*/
suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String
/**
* Unloads the currently loaded model.
*/
suspend fun cleanUp()
/**
* Cleans up resources when the engine is no longer needed.
*/
fun destroy()
/**
* States of the inference engine
*/
sealed class State {
object Uninitialized : State()
object Initializing : State()
object Initialized : State()
object LoadingModel : State()
object UnloadingModel : State()
object ModelReady : State()
object Benchmarking : State()
object ProcessingSystemPrompt : State()
object ProcessingUserPrompt : State()
object Generating : State()
data class Error(val exception: Exception) : State()
}
companion object {
const val DEFAULT_PREDICT_LENGTH = 1024
}
}
val State.isUninterruptible
get() = this is State.Initializing ||
this is State.LoadingModel ||
this is State.UnloadingModel ||
this is State.Benchmarking ||
this is State.ProcessingSystemPrompt ||
this is State.ProcessingUserPrompt
val State.isModelLoaded: Boolean
get() = this is State.ModelReady ||
this is State.Benchmarking ||
this is State.ProcessingSystemPrompt ||
this is State.ProcessingUserPrompt ||
this is State.Generating
class UnsupportedArchitectureException : Exception()

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examples/llama.android/lib/src/main/java/com/arm/aichat/gguf/FileType.kt Normal file
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package com.arm.aichat.gguf
import kotlin.collections.get
/**
* Numerical codes used by `general.file_type` (see llama.cpp repo's `constants.py`).
* The `label` matches what llama‑cli prints.
*/
enum class FileType(val code: Int, val label: String) {
ALL_F32(0, "all F32"),
MOSTLY_F16(1, "F16"),
MOSTLY_Q4_0(2, "Q4_0"),
MOSTLY_Q4_1(3, "Q4_1"),
// 4 removed
MOSTLY_Q8_0(7, "Q8_0"),
MOSTLY_Q5_0(8, "Q5_0"),
MOSTLY_Q5_1(9, "Q5_1"),
/* K‑quants ------------------------------------------------------------ */
MOSTLY_Q2_K (10, "Q2_K - Medium"),
MOSTLY_Q3_K_S (11, "Q3_K - Small"),
MOSTLY_Q3_K_M (12, "Q3_K - Medium"),
MOSTLY_Q3_K_L (13, "Q3_K - Large"),
MOSTLY_Q4_K_S (14, "Q4_K - Small"),
MOSTLY_Q4_K_M (15, "Q4_K - Medium"),
MOSTLY_Q5_K_S (16, "Q5_K - Small"),
MOSTLY_Q5_K_M (17, "Q5_K - Medium"),
MOSTLY_Q6_K (18, "Q6_K"),
/* IQ quants ----------------------------------------------------------- */
MOSTLY_IQ2_XXS (19, "IQ2_XXS - 2.06 bpw"),
MOSTLY_IQ2_XS (20, "IQ2_XS - 2.31 bpw"),
MOSTLY_Q2_K_S (21, "Q2_K - Small"),
MOSTLY_IQ3_XS (22, "IQ3_XS - 3.30 bpw"),
MOSTLY_IQ3_XXS (23, "IQ3_XXS - 3.06 bpw"),
MOSTLY_IQ1_S (24, "IQ1_S - 1.56 bpw"),
MOSTLY_IQ4_NL (25, "IQ4_NL - 4.5 bpw"),
MOSTLY_IQ3_S (26, "IQ3_S - 3.44 bpw"),
MOSTLY_IQ3_M (27, "IQ3_M - 3.66 bpw"),
MOSTLY_IQ2_S (28, "IQ2_S - 2.50 bpw"),
MOSTLY_IQ2_M (29, "IQ2_M - 2.70 bpw"),
MOSTLY_IQ4_XS (30, "IQ4_XS - 4.25 bpw"),
MOSTLY_IQ1_M (31, "IQ1_M - 1.75 bpw"),
/* BF16 & Ternary ------------------------------------------------------ */
MOSTLY_BF16 (32, "BF16"),
MOSTLY_TQ1_0 (36, "TQ1_0 - 1.69 bpw ternary"),
MOSTLY_TQ2_0 (37, "TQ2_0 - 2.06 bpw ternary"),
/* Special flag -------------------------------------------------------- */
GUESSED(1024, "(guessed)"),
UNKNOWN(-1, "unknown");
companion object {
private val map = entries.associateBy(FileType::code)
fun fromCode(code: Int?): FileType = map[code] ?: UNKNOWN
}
}

132
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package com.arm.aichat.gguf
import java.io.IOException
/**
* Structured metadata of GGUF
*/
data class GgufMetadata(
// Basic file info
val version: GgufVersion,
val tensorCount: Long,
val kvCount: Long,
// General info
val basic: BasicInfo,
val author: AuthorInfo? = null,
val additional: AdditionalInfo? = null,
val architecture: ArchitectureInfo? = null,
val baseModels: List<BaseModelInfo>? = null,
val tokenizer: TokenizerInfo? = null,
// Derivative info
val dimensions: DimensionsInfo? = null,
val attention: AttentionInfo? = null,
val rope: RopeInfo? = null,
val experts: ExpertsInfo? = null
) {
enum class GgufVersion(val code: Int, val label: String) {
/** First public draft; little‑endian only, no alignment key. */
LEGACY_V1(1, "Legacy v1"),
/** Added split‑file support and some extra metadata keys. */
EXTENDED_V2(2, "Extended v2"),
/** Current spec: endian‑aware, mandatory alignment, fully validated. */
VALIDATED_V3(3, "Validated v3");
companion object {
fun fromCode(code: Int): GgufVersion =
entries.firstOrNull { it.code == code }
?: throw IOException("Unknown GGUF version code $code")
}
override fun toString(): String = "$label (code=$code)"
}
data class BasicInfo(
val uuid: String? = null,
val name: String? = null,
val nameLabel: String? = null,
val sizeLabel: String? = null, // Size label like "7B"
)
data class AuthorInfo(
val organization: String? = null,
val author: String? = null,
val doi: String? = null,
val url: String? = null,
val repoUrl: String? = null,
val license: String? = null,
val licenseLink: String? = null,
)
data class AdditionalInfo(
val type: String? = null,
val description: String? = null,
val tags: List<String>? = null,
val languages: List<String>? = null,
)
data class ArchitectureInfo(
val architecture: String? = null,
val fileType: Int? = null,
val vocabSize: Int? = null,
val finetune: String? = null,
val quantizationVersion: Int? = null,
)
data class BaseModelInfo(
val name: String? = null,
val author: String? = null,
val version: String? = null,
val organization: String? = null,
val url: String? = null,
val doi: String? = null,
val uuid: String? = null,
val repoUrl: String? = null,
)
data class TokenizerInfo(
val model: String? = null,
val bosTokenId: Int? = null,
val eosTokenId: Int? = null,
val unknownTokenId: Int? = null,
val paddingTokenId: Int? = null,
val addBosToken: Boolean? = null,
val addEosToken: Boolean? = null,
val chatTemplate: String? = null,
)
data class DimensionsInfo(
val contextLength: Int? = null,
val embeddingSize: Int? = null,
val blockCount: Int? = null,
val feedForwardSize: Int? = null,
)
data class AttentionInfo(
val headCount: Int? = null,
val headCountKv: Int? = null,
val keyLength: Int? = null,
val valueLength: Int? = null,
val layerNormEpsilon: Float? = null,
val layerNormRmsEpsilon: Float? = null,
)
data class RopeInfo(
val frequencyBase: Float? = null,
val dimensionCount: Int? = null,
val scalingType: String? = null,
val scalingFactor: Float? = null,
val attnFactor: Float? = null,
val originalContextLength: Int? = null,
val finetuned: Boolean? = null,
)
data class ExpertsInfo(
val count: Int? = null,
val usedCount: Int? = null,
)
}

77
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package com.arm.aichat.gguf
import android.content.Context
import android.net.Uri
import com.arm.aichat.internal.gguf.GgufMetadataReaderImpl
import java.io.File
import java.io.IOException
import java.io.InputStream
/**
* Interface for reading GGUF metadata from model files.
* Use `GgufMetadataReader.create()` to get an instance.
*/
interface GgufMetadataReader {
/**
* Reads the magic number from the specified file path.
*
* @param file Java File to the GGUF file with absolute path
* @return true if file is valid GGUF, otherwise false
* @throws InvalidFileFormatException if file format is invalid
*/
suspend fun ensureSourceFileFormat(file: File): Boolean
/**
* Reads the magic number from the specified file path.
*
* @param context Context for obtaining [android.content.ContentProvider]
* @param uri Uri to the GGUF file provided by [android.content.ContentProvider]
* @return true if file is valid GGUF, otherwise false
* @throws InvalidFileFormatException if file format is invalid
*/
suspend fun ensureSourceFileFormat(context: Context, uri: Uri): Boolean
/**
* Reads and parses GGUF metadata from the specified file path.
*
* @param input the [InputStream] obtained from a readable file or content
* @return Structured metadata extracted from the file
* @throws IOException if file is damaged or cannot be read
* @throws InvalidFileFormatException if file format is invalid
*/
suspend fun readStructuredMetadata(input: InputStream): GgufMetadata
companion object {
private val DEFAULT_SKIP_KEYS = setOf(
"tokenizer.chat_template",
"tokenizer.ggml.scores",
"tokenizer.ggml.tokens",
"tokenizer.ggml.token_type"
)
/**
* Creates a default GgufMetadataReader instance
*/
fun create(): GgufMetadataReader = GgufMetadataReaderImpl(
skipKeys = DEFAULT_SKIP_KEYS,
arraySummariseThreshold = 1_000
)
/**
* Creates a GgufMetadataReader with custom configuration
*
* @param skipKeys Keys whose value should be skipped entirely (not kept in the result map)
* @param arraySummariseThreshold If ≥0, arrays longer get summarised, not materialised;
* If -1, never summarise.
*/
fun create(
skipKeys: Set<String> = DEFAULT_SKIP_KEYS,
arraySummariseThreshold: Int = 1_000
): GgufMetadataReader = GgufMetadataReaderImpl(
skipKeys = skipKeys,
arraySummariseThreshold = arraySummariseThreshold
)
}
}
class InvalidFileFormatException : IOException()

309
examples/llama.android/lib/src/main/java/com/arm/aichat/internal/InferenceEngineImpl.kt Normal file
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package com.arm.aichat.internal
import android.content.Context
import android.util.Log
import com.arm.aichat.InferenceEngine
import com.arm.aichat.UnsupportedArchitectureException
import com.arm.aichat.internal.InferenceEngineImpl.Companion.getInstance
import dalvik.annotation.optimization.FastNative
import kotlinx.coroutines.CancellationException
import kotlinx.coroutines.CoroutineScope
import kotlinx.coroutines.Dispatchers
import kotlinx.coroutines.ExperimentalCoroutinesApi
import kotlinx.coroutines.SupervisorJob
import kotlinx.coroutines.cancel
import kotlinx.coroutines.flow.Flow
import kotlinx.coroutines.flow.MutableStateFlow
import kotlinx.coroutines.flow.StateFlow
import kotlinx.coroutines.flow.flow
import kotlinx.coroutines.flow.flowOn
import kotlinx.coroutines.launch
import kotlinx.coroutines.withContext
import java.io.File
import java.io.IOException
/**
* JNI wrapper for the llama.cpp library providing Android-friendly access to large language models.
*
* This class implements a singleton pattern for managing the lifecycle of a single LLM instance.
* All operations are executed on a dedicated single-threaded dispatcher to ensure thread safety
* with the underlying C++ native code.
*
* The typical usage flow is:
* 1. Get instance via [getInstance]
* 2. Load a model with [loadModel]
* 3. Send prompts with [sendUserPrompt]
* 4. Generate responses as token streams
* 5. Perform [cleanUp] when done with a model
* 6. Properly [destroy] when completely done
*
* State transitions are managed automatically and validated at each operation.
*
* @see ai_chat.cpp for the native implementation details
*/
internal class InferenceEngineImpl private constructor(
private val nativeLibDir: String
) : InferenceEngine {
companion object {
private val TAG = InferenceEngineImpl::class.java.simpleName
@Volatile
private var instance: InferenceEngine? = null
/**
* Create or obtain [InferenceEngineImpl]'s single instance.
*
* @param Context for obtaining native library directory
* @throws IllegalArgumentException if native library path is invalid
* @throws UnsatisfiedLinkError if library failed to load
*/
internal fun getInstance(context: Context) =
instance ?: synchronized(this) {
val nativeLibDir = context.applicationInfo.nativeLibraryDir
require(nativeLibDir.isNotBlank()) { "Expected a valid native library path!" }
try {
Log.i(TAG, "Instantiating InferenceEngineImpl,,,")
InferenceEngineImpl(nativeLibDir).also { instance = it }
} catch (e: UnsatisfiedLinkError) {
Log.e(TAG, "Failed to load native library from $nativeLibDir", e)
throw e
}
}
}
/**
* JNI methods
* @see ai_chat.cpp
*/
@FastNative
private external fun init(nativeLibDir: String)
@FastNative
private external fun load(modelPath: String): Int
@FastNative
private external fun prepare(): Int
@FastNative
private external fun systemInfo(): String
@FastNative
private external fun benchModel(pp: Int, tg: Int, pl: Int, nr: Int): String
@FastNative
private external fun processSystemPrompt(systemPrompt: String): Int
@FastNative
private external fun processUserPrompt(userPrompt: String, predictLength: Int): Int
@FastNative
private external fun generateNextToken(): String?
@FastNative
private external fun unload()
@FastNative
private external fun shutdown()
private val _state =
MutableStateFlow<InferenceEngine.State>(InferenceEngine.State.Uninitialized)
override val state: StateFlow<InferenceEngine.State> = _state
private var _readyForSystemPrompt = false
/**
* Single-threaded coroutine dispatcher & scope for LLama asynchronous operations
*/
@OptIn(ExperimentalCoroutinesApi::class)
private val llamaDispatcher = Dispatchers.IO.limitedParallelism(1)
private val llamaScope = CoroutineScope(llamaDispatcher + SupervisorJob())
init {
llamaScope.launch {
try {
check(_state.value is InferenceEngine.State.Uninitialized) {
"Cannot load native library in ${_state.value.javaClass.simpleName}!"
}
_state.value = InferenceEngine.State.Initializing
Log.i(TAG, "Loading native library...")
System.loadLibrary("ai-chat")
init(nativeLibDir)
_state.value = InferenceEngine.State.Initialized
Log.i(TAG, "Native library loaded! System info: \n${systemInfo()}")
} catch (e: Exception) {
Log.e(TAG, "Failed to load native library", e)
throw e
}
}
}
/**
* Load the LLM
*/
override suspend fun loadModel(pathToModel: String) =
withContext(llamaDispatcher) {
check(_state.value is InferenceEngine.State.Initialized) {
"Cannot load model in ${_state.value.javaClass.simpleName}!"
}
try {
Log.i(TAG, "Checking access to model file... \n$pathToModel")
File(pathToModel).let {
require(it.exists()) { "File not found" }
require(it.isFile) { "Not a valid file" }
require(it.canRead()) { "Cannot read file" }
}
Log.i(TAG, "Loading model... \n$pathToModel")
_readyForSystemPrompt = false
_state.value = InferenceEngine.State.LoadingModel
load(pathToModel).let {
// TODO-han.yin: find a better way to pass other error codes
if (it != 0) throw UnsupportedArchitectureException()
}
prepare().let {
if (it != 0) throw IOException("Failed to prepare resources")
}
Log.i(TAG, "Model loaded!")
_readyForSystemPrompt = true
_state.value = InferenceEngine.State.ModelReady
} catch (e: Exception) {
Log.e(TAG, (e.message ?: "Error loading model") + "\n" + pathToModel, e)
_state.value = InferenceEngine.State.Error(e)
throw e
}
}
/**
* Process the plain text system prompt
*
* TODO-han.yin: return error code if system prompt not correct processed?
*/
override suspend fun setSystemPrompt(prompt: String) =
withContext(llamaDispatcher) {
require(prompt.isNotBlank()) { "Cannot process empty system prompt!" }
check(_readyForSystemPrompt) { "System prompt must be set ** RIGHT AFTER ** model loaded!" }
check(_state.value is InferenceEngine.State.ModelReady) {
"Cannot process system prompt in ${_state.value.javaClass.simpleName}!"
}
Log.i(TAG, "Sending system prompt...")
_readyForSystemPrompt = false
_state.value = InferenceEngine.State.ProcessingSystemPrompt
processSystemPrompt(prompt).let { result ->
if (result != 0) {
RuntimeException("Failed to process system prompt: $result").also {
_state.value = InferenceEngine.State.Error(it)
throw it
}
}
}
Log.i(TAG, "System prompt processed! Awaiting user prompt...")
_state.value = InferenceEngine.State.ModelReady
}
/**
* Send plain text user prompt to LLM, which starts generating tokens in a [Flow]
*/
override fun sendUserPrompt(
message: String,
predictLength: Int,
): Flow<String> = flow {
require(message.isNotEmpty()) { "User prompt discarded due to being empty!" }
check(_state.value is InferenceEngine.State.ModelReady) {
"User prompt discarded due to: ${_state.value.javaClass.simpleName}"
}
try {
Log.i(TAG, "Sending user prompt...")
_readyForSystemPrompt = false
_state.value = InferenceEngine.State.ProcessingUserPrompt
processUserPrompt(message, predictLength).let { result ->
if (result != 0) {
Log.e(TAG, "Failed to process user prompt: $result")
return@flow
}
}
Log.i(TAG, "User prompt processed. Generating assistant prompt...")
_state.value = InferenceEngine.State.Generating
while (true) {
generateNextToken()?.let { utf8token ->
if (utf8token.isNotEmpty()) emit(utf8token)
} ?: break
}
Log.i(TAG, "Assistant generation complete. Awaiting user prompt...")
_state.value = InferenceEngine.State.ModelReady
} catch (e: CancellationException) {
Log.i(TAG, "Generation cancelled by user.")
_state.value = InferenceEngine.State.ModelReady
throw e
} catch (e: Exception) {
Log.e(TAG, "Error during generation!", e)
_state.value = InferenceEngine.State.Error(e)
throw e
}
}.flowOn(llamaDispatcher)
/**
* Benchmark the model
*/
override suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int): String =
withContext(llamaDispatcher) {
check(_state.value is InferenceEngine.State.ModelReady) {
"Benchmark request discarded due to: $state"
}
Log.i(TAG, "Start benchmark (pp: $pp, tg: $tg, pl: $pl, nr: $nr)")
_readyForSystemPrompt = false // Just to be safe
_state.value = InferenceEngine.State.Benchmarking
benchModel(pp, tg, pl, nr).also {
_state.value = InferenceEngine.State.ModelReady
}
}
/**
* Unloads the model and frees resources, or reset error states
*/
override suspend fun cleanUp() =
withContext(llamaDispatcher) {
when (val state = _state.value) {
is InferenceEngine.State.ModelReady -> {
Log.i(TAG, "Unloading model and free resources...")
_readyForSystemPrompt = false
_state.value = InferenceEngine.State.UnloadingModel
unload()
_state.value = InferenceEngine.State.Initialized
Log.i(TAG, "Model unloaded!")
Unit
}
is InferenceEngine.State.Error -> {
Log.i(TAG, "Resetting error states...")
_state.value = InferenceEngine.State.Initialized
Log.i(TAG, "States reset!")
Unit
}
else -> throw IllegalStateException("Cannot unload model in ${state.javaClass.simpleName}")
}
}
/**
* Cancel all ongoing coroutines and free GGML backends
*/
override fun destroy() {
_readyForSystemPrompt = false
llamaScope.cancel()
when(_state.value) {
is InferenceEngine.State.Uninitialized -> {}
is InferenceEngine.State.Initialized -> shutdown()
else -> { unload(); shutdown() }
}
}
}

590
examples/llama.android/lib/src/main/java/com/arm/aichat/internal/gguf/GgufMetadataReaderImpl.kt Normal file
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package com.arm.aichat.internal.gguf
import android.content.Context
import android.net.Uri
import com.arm.aichat.gguf.GgufMetadata
import com.arm.aichat.gguf.GgufMetadataReader
import com.arm.aichat.gguf.InvalidFileFormatException
import java.io.File
import java.io.IOException
import java.io.InputStream
/**
* Utility class to read GGUF model files and extract metadata key-value pairs.
* This parser reads the header and metadata of a GGUF v3 file (little-endian) and skips tensor data.
*/
internal class GgufMetadataReaderImpl(
private val skipKeys: Set<String>,
private val arraySummariseThreshold: Int,
) : GgufMetadataReader {
companion object {
private const val ARCH_LLAMA = "llama"
}
/** Enum corresponding to GGUF metadata value types (for convenience and array element typing). */
enum class MetadataType(val code: Int) {
UINT8(0), INT8(1), UINT16(2), INT16(3),
UINT32(4), INT32(5), FLOAT32(6), BOOL(7),
STRING(8), ARRAY(9), UINT64(10), INT64(11), FLOAT64(12);
companion object {
private val codeMap = entries.associateBy(MetadataType::code)
fun fromCode(code: Int): MetadataType = codeMap[code]
?: throw IOException("Unknown metadata value type code: $code")
}
}
/** Sealed class hierarchy for metadata values, providing type-safe representations for each GGUF metadata type. */
sealed class MetadataValue {
data class UInt8(val value: UByte) : MetadataValue() // 0: 8-bit unsigned int
data class Int8(val value: Byte) : MetadataValue() // 1: 8-bit signed int
data class UInt16(val value: UShort) : MetadataValue() // 2: 16-bit unsigned int (little-endian)
data class Int16(val value: Short) : MetadataValue() // 3: 16-bit signed int (little-endian)
data class UInt32(val value: UInt) : MetadataValue() // 4: 32-bit unsigned int (little-endian)
data class Int32(val value: Int) : MetadataValue() // 5: 32-bit signed int (little-endian)
data class Float32(val value: Float) : MetadataValue() // 6: 32-bit IEEE754 float
data class Bool(val value: Boolean) : MetadataValue() // 7: Boolean (1-byte, 0=false, 1=true)
data class StringVal(val value: String) : MetadataValue() // 8: UTF-8 string (length-prefixed)
data class ArrayVal(val elementType: MetadataType, val elements: List<MetadataValue>) : MetadataValue()
data class UInt64(val value: ULong) : MetadataValue() // 10: 64-bit unsigned int (little-endian)
data class Int64(val value: Long) : MetadataValue() // 11: 64-bit signed int (little-endian)
data class Float64(val value: Double) : MetadataValue() // 12: 64-bit IEEE754 double
}
/* Convert MetadataValue to plain Kotlin primitives for allMetadata map */
private fun MetadataValue.toPrimitive(): Any = when (this) {
is MetadataValue.UInt8 -> value
is MetadataValue.Int8 -> value
is MetadataValue.UInt16 -> value
is MetadataValue.Int16 -> value
is MetadataValue.UInt32 -> value
is MetadataValue.Int32 -> value
is MetadataValue.Float32 -> value
is MetadataValue.Bool -> value
is MetadataValue.StringVal -> value
is MetadataValue.UInt64 -> value
is MetadataValue.Int64 -> value
is MetadataValue.Float64 -> value
is MetadataValue.ArrayVal -> elements.map { it.toPrimitive() }
}
/**
* Reads the magic number from the specified file path.
*
* @param context Context for obtaining ContentResolver
* @param uri Uri to the GGUF file provided by ContentProvider
* @return true if file is valid GGUF, otherwise false
*/
override suspend fun ensureSourceFileFormat(file: File): Boolean =
file.inputStream().buffered().use { ensureMagic(it) }
/**
* Reads the magic number from the specified file path.
*
* @param context Context for obtaining ContentResolver
* @param uri Uri to the GGUF file provided by ContentProvider
* @return true if file is valid GGUF, otherwise false
*/
override suspend fun ensureSourceFileFormat(context: Context, uri: Uri): Boolean =
context.contentResolver.openInputStream(uri)?.buffered()?.use { ensureMagic(it) } == true
/** Reads the 4‑byte magic; throws if magic ≠ "GGUF". */
private fun ensureMagic(input: InputStream): Boolean =
ByteArray(4).let {
if (input.read(it) != 4) throw IOException("Not a valid file!")
it.contentEquals(byteArrayOf(0x47, 0x47, 0x55, 0x46)) // "GGUF"
}
/**
* High‑level entry point: parses a `.gguf` file on disk and returns the fully
* populated [GgufMetadata] tree.
*
* Steps performed internally:
* 1. Reads and validates the 8‑byte header (`"GGUF"` magic + version).
* 2. Streams through the key‑value section, skipping large blobs if the key
* appears in [skipKeys] or if an array exceeds [arraySummariseThreshold].
* 3. Converts the resulting raw map into strongly‑typed sub‑structures
* (basic info, tokenizer, rope, etc.).
*
* The method is STREAMING‑ONLY: tensors are never mapped or loaded into
* memory, so even multi‑GB model files can be processed in < 50 ms.
*
* @param path Absolute or relative filesystem path to a `.gguf` file.
* @return A [GgufMetadata] instance containing all recognised metadata plus
* an `allMetadata` map with any keys that were not given a dedicated
* field.
* @throws IOException if the file is not GGUF, the version is unsupported,
* or the metadata block is truncated / corrupt.
*/
override suspend fun readStructuredMetadata(input: InputStream): GgufMetadata {
// ── 1. header ──────────────────────────────────────────────────────────
// throws on mismatch
val version = ensureMagicAndVersion(input)
val tensorCount = readLittleLong(input)
val kvCount = readLittleLong(input)
// ── 2. metadata map (reuse our raw parser, but we need access to the stream) ──
val meta = readMetaMap(input, kvCount) // <String, MetadataValue>
// ── 3. build structured object ────────────────────────────────────────
return buildStructured(meta, version, tensorCount, kvCount)
}
/** Reads the 4‑byte magic + 4‑byte version; throws if magic ≠ "GGUF". */
private fun ensureMagicAndVersion(input: InputStream): GgufMetadata.GgufVersion {
if (!ensureMagic(input)) throw InvalidFileFormatException()
return GgufMetadata.GgufVersion.fromCode(readLEUInt32(input))
}
/**
* Read an unsigned 32‑bit little‑endian integer.
*
* @throws IOException if fewer than four bytes are available.
*/
private fun readLEUInt32(input: InputStream): Int {
val b0 = input.read(); val b1 = input.read(); val b2 = input.read(); val b3 = input.read()
if (b3 == -1) throw IOException("Unexpected EOF while reading UInt32")
return (b3 and 0xFF shl 24) or
(b2 and 0xFF shl 16) or
(b1 and 0xFF shl 8) or
(b0 and 0xFF)
}
/**
* Low‑level helper that reads the entire “key-value” section from the current
* stream position.
*
* @param input Open stream positioned JUST AFTER the header.
* @param kvCnt Number of key‑value pairs (taken from the header).
* @return Mutable map with one [MetadataValue] for every key that is NOT skipped.
*
* The function honours [skipKeys] and [arraySummariseThreshold] by invoking
* [skipValue] or [parseValue] accordingly.
*/
private fun readMetaMap(input: InputStream, kvCnt: Long): Map<String, MetadataValue> =
mutableMapOf<String, MetadataValue>().apply {
repeat(kvCnt.toInt()) {
val key = readString(input)
val valueT = MetadataType.fromCode(littleEndianBytesToInt(input.readNBytesExact(4)))
if (key in skipKeys) {
skipValue(input, valueT)
} else {
this[key] = parseValue(input, valueT)
}
}
}
/**
* Converts a flat [Map]<[String], [MetadataValue]> into the strongly‑typed
* [GgufMetadata] tree used by the rest of the app.
*
* Only the keys listed in the spec are copied into dedicated data classes;
* everything else is preserved in `GgufMetadata.allMetadata`.
*
* @param m Raw key/value map.
* @param version GGUF file‑format version (enum).
* @param tensorCnt Number of tensors (from the header).
* @param kvCnt Total metadata pair count (from the header).
*/
private fun buildStructured(
m: Map<String, MetadataValue>,
version: GgufMetadata.GgufVersion,
tensorCnt: Long,
kvCnt: Long
): GgufMetadata {
// ---------- helpers ----------
fun String.str() = (m[this] as? MetadataValue.StringVal)?.value
fun String.bool() = (m[this] as? MetadataValue.Bool)?.value
fun String.i32() = (m[this] as? MetadataValue.Int32)?.value
fun String.u32() = (m[this] as? MetadataValue.UInt32)?.value?.toInt()
fun String.f32() = (m[this] as? MetadataValue.Float32)?.value
fun String.f64() = (m[this] as? MetadataValue.Float64)?.value?.toFloat()
fun String.strList(): List<String>? =
(m[this] as? MetadataValue.ArrayVal)
?.elements
?.mapNotNull { (it as? MetadataValue.StringVal)?.value }
val arch = "general.architecture".str() ?: ARCH_LLAMA
// -------------- populate sections ----------------
val basic = GgufMetadata.BasicInfo(
uuid = "general.uuid".str(),
name = "general.basename".str(),
nameLabel = "general.name".str(),
sizeLabel = "general.size_label".str()
)
val author = GgufMetadata.AuthorInfo(
organization = "general.organization".str(),
author = "general.author".str(),
doi = "general.doi".str(),
url = "general.url".str(),
repoUrl = "general.repo_url".str(),
license = "general.license".str(),
licenseLink = "general.license.link".str()
).takeUnless {
organization == null && author == null && doi == null &&
url == null && repoUrl == null && license == null && licenseLink == null
}
val additional = GgufMetadata.AdditionalInfo(
type = "general.type".str(),
description = "general.description".str(),
tags = "general.tags".strList(),
languages = "general.languages".strList()
).takeUnless {
type == null && description == null && tags == null && languages == null
}
val architectureInfo = GgufMetadata.ArchitectureInfo(
architecture = arch,
fileType = "general.file_type".u32(),
vocabSize = "$arch.vocab_size".u32(),
finetune = "general.finetune".str(),
quantizationVersion = "general.quantization_version".u32()
).takeUnless { fileType == null && vocabSize == null && finetune == null && quantizationVersion == null }
val baseModels = buildList {
val n = "general.base_model.count".u32() ?: 0
for (i in 0 until n) {
fun k(s: String) = "general.base_model.$i.$s"
add(
GgufMetadata.BaseModelInfo(
name = k("name").str(),
author = k("author").str(),
version = k("version").str(),
organization = k("organization").str(),
url = k("url").str(),
doi = k("doi").str(),
uuid = k("uuid").str(),
repoUrl = k("repo_url").str(),
)
)
}
}.takeIf { it.isNotEmpty() }
val tokenizer = GgufMetadata.TokenizerInfo(
model = "tokenizer.ggml.model".str(),
bosTokenId = "tokenizer.ggml.bos_token_id".u32(),
eosTokenId = "tokenizer.ggml.eos_token_id".u32(),
unknownTokenId = "tokenizer.ggml.unknown_token_id".u32(),
paddingTokenId = "tokenizer.ggml.padding_token_id".u32(),
addBosToken = "tokenizer.ggml.add_bos_token".bool(),
addEosToken = "tokenizer.ggml.add_eos_token".bool(),
chatTemplate = "tokenizer.chat_template".str()
).takeUnless { model == null && bosTokenId == null && eosTokenId == null &&
unknownTokenId == null && paddingTokenId == null &&
addBosToken == null && addEosToken == null && chatTemplate == null
}
val dimensions = GgufMetadata.DimensionsInfo(
contextLength = "$arch.context_length".u32(),
embeddingSize = "$arch.embedding_length".u32(),
blockCount = "$arch.block_count".u32(),
feedForwardSize = "$arch.feed_forward_length".u32()
).takeUnless { contextLength == null && embeddingSize == null && blockCount == null && feedForwardSize == null }
val attention = GgufMetadata.AttentionInfo(
headCount = "$arch.attention.head_count".u32(),
headCountKv = "$arch.attention.head_count_kv".u32(),
keyLength = "$arch.attention.key_length".u32(),
valueLength = "$arch.attention.value_length".u32(),
layerNormEpsilon = "$arch.attention.layer_norm_epsilon".f32(),
layerNormRmsEpsilon = "$arch.attention.layer_norm_rms_epsilon".f32(),
).takeUnless { headCount == null && headCountKv == null && keyLength == null && valueLength == null &&
layerNormEpsilon == null && layerNormRmsEpsilon == null
}
val rope = GgufMetadata.RopeInfo(
frequencyBase = "$arch.rope.freq_base".f32(),
dimensionCount = "$arch.rope.dimension_count".u32(),
scalingType = "$arch.rope.scaling.type".str(),
scalingFactor = "$arch.rope.scaling.factor".f32(),
attnFactor = "$arch.rope.scaling.attn_factor".f32(),
originalContextLength = "$arch.rope.scaling.original_context_length".u32(),
finetuned = "$arch.rope.scaling.finetuned".bool()
).takeUnless { frequencyBase == null && dimensionCount == null &&
scalingType == null && scalingFactor == null && attnFactor == null &&
originalContextLength == null && finetuned == null
}
val experts = GgufMetadata.ExpertsInfo(
count = "$arch.expert_count".u32(),
usedCount = "$arch.expert_used_count".u32()
).takeUnless { count == null && usedCount == null }
return GgufMetadata(
version = version,
tensorCount = tensorCnt,
kvCount = kvCnt,
basic = basic,
author = author,
additional = additional,
architecture = architectureInfo,
baseModels = baseModels,
tokenizer = tokenizer,
dimensions = dimensions,
attention = attention,
rope = rope,
experts = experts
)
}
/**
* Recursively parses a metadata value of the given type from the input stream.
* @param input The input stream positioned at the start of the value.
* @param type The metadata value type to parse.
*/
private fun parseValue(input: InputStream, type: MetadataType): MetadataValue = when (type) {
MetadataType.UINT8 -> {
// 1-byte unsigned integer
val byteVal = input.read()
if (byteVal == -1) throw IOException("Unexpected EOF while reading uint8 value.")
MetadataValue.UInt8(byteVal.toUByte())
}
MetadataType.INT8 -> {
// 1-byte signed integer
val byteVal = input.read()
if (byteVal == -1) throw IOException("Unexpected EOF while reading int8 value.")
MetadataValue.Int8(byteVal.toByte())
}
MetadataType.UINT16 -> {
// 2-byte unsigned integer (little-endian)
val bytes = ByteArray(2)
if (input.read(bytes) != 2) throw IOException("Unexpected EOF while reading uint16 value.")
// Combine two bytes (little-endian) into an unsigned 16-bit value
val u16 = ((bytes[1].toInt() and 0xFF) shl 8) or (bytes[0].toInt() and 0xFF)
MetadataValue.UInt16(u16.toUShort())
}
MetadataType.INT16 -> {
// 2-byte signed integer (little-endian)
val bytes = ByteArray(2)
if (input.read(bytes) != 2) throw IOException("Unexpected EOF while reading int16 value.")
// Combine to 16-bit and interpret as signed
val i16 = ((bytes[1].toInt() and 0xFF) shl 8) or (bytes[0].toInt() and 0xFF)
MetadataValue.Int16(i16.toShort())
}
MetadataType.UINT32 -> {
// 4-byte unsigned integer (little-endian)
val bytes = ByteArray(4)
if (input.read(bytes) != 4) throw IOException("Unexpected EOF while reading uint32 value.")
// Combine four bytes into a 32-bit value (as Long to avoid overflow), then convert to UInt
val u32 = (bytes[3].toLong() and 0xFFL shl 24) or
(bytes[2].toLong() and 0xFFL shl 16) or
(bytes[1].toLong() and 0xFFL shl 8) or
(bytes[0].toLong() and 0xFFL)
MetadataValue.UInt32(u32.toUInt())
}
MetadataType.INT32 -> {
// 4-byte signed integer (little-endian)
val bytes = ByteArray(4)
if (input.read(bytes) != 4) throw IOException("Unexpected EOF while reading int32 value.")
// Combine four bytes into a 32-bit signed int
val i32 = (bytes[3].toInt() and 0xFF shl 24) or
(bytes[2].toInt() and 0xFF shl 16) or
(bytes[1].toInt() and 0xFF shl 8) or
(bytes[0].toInt() and 0xFF)
MetadataValue.Int32(i32)
}
MetadataType.FLOAT32 -> {
// 4-byte IEEE 754 float (little-endian)
val bytes = ByteArray(4)
if (input.read(bytes) != 4) throw IOException("Unexpected EOF while reading float32 value.")
// Assemble 4 bytes into a 32-bit int bit-pattern, then convert to Float
val bits = (bytes[3].toInt() and 0xFF shl 24) or
(bytes[2].toInt() and 0xFF shl 16) or
(bytes[1].toInt() and 0xFF shl 8) or
(bytes[0].toInt() and 0xFF)
val floatVal = Float.fromBits(bits)
MetadataValue.Float32(floatVal)
}
MetadataType.BOOL -> {
// 1-byte boolean (0 = false, 1 = true)
val byteVal = input.read()
if (byteVal == -1) throw IOException("Unexpected EOF while reading boolean value.")
if (byteVal != 0 && byteVal != 1) {
throw IOException("Invalid boolean value: $byteVal (must be 0 or 1).")
}
MetadataValue.Bool(byteVal != 0)
}
MetadataType.STRING -> {
// UTF-8 string (length-prefixed with 8-byte length)
val str = readString(input)
MetadataValue.StringVal(str)
}
MetadataType.ARRAY -> {
val elemType = MetadataType.fromCode(littleEndianBytesToInt(input.readNBytesExact(4)))
val len = readLittleLong(input)
val count = len.toInt()
if (arraySummariseThreshold >= 0 && count > arraySummariseThreshold) {
// fast‑forward without allocation
repeat(count) { skipValue(input, elemType) }
MetadataValue.StringVal("Array($elemType, $count items) /* summarised */")
} else {
val list = ArrayList<MetadataValue>(count)
repeat(count) { list += parseValue(input, elemType) }
MetadataValue.ArrayVal(elemType, list)
}
}
MetadataType.UINT64 -> {
// 8-byte unsigned integer (little-endian)
val bytes = ByteArray(8)
if (input.read(bytes) != 8) throw IOException("Unexpected EOF while reading uint64 value.")
// Combine 8 bytes into an unsigned 64-bit (ULong). Use ULong for full 0 to 2^64-1 range.
val u64 = (bytes[7].toULong() and 0xFFuL shl 56) or
(bytes[6].toULong() and 0xFFuL shl 48) or
(bytes[5].toULong() and 0xFFuL shl 40) or
(bytes[4].toULong() and 0xFFuL shl 32) or
(bytes[3].toULong() and 0xFFuL shl 24) or
(bytes[2].toULong() and 0xFFuL shl 16) or
(bytes[1].toULong() and 0xFFuL shl 8) or
(bytes[0].toULong() and 0xFFuL)
MetadataValue.UInt64(u64)
}
MetadataType.INT64 -> {
// 8-byte signed integer (little-endian)
val bytes = ByteArray(8)
if (input.read(bytes) != 8) throw IOException("Unexpected EOF while reading int64 value.")
// Combine 8 bytes into a signed 64-bit value (Long)
val i64 = (bytes[7].toLong() and 0xFFL shl 56) or
(bytes[6].toLong() and 0xFFL shl 48) or
(bytes[5].toLong() and 0xFFL shl 40) or
(bytes[4].toLong() and 0xFFL shl 32) or
(bytes[3].toLong() and 0xFFL shl 24) or
(bytes[2].toLong() and 0xFFL shl 16) or
(bytes[1].toLong() and 0xFFL shl 8) or
(bytes[0].toLong() and 0xFFL)
MetadataValue.Int64(i64)
}
MetadataType.FLOAT64 -> {
// 8-byte IEEE 754 double (little-endian)
val bytes = ByteArray(8)
if (input.read(bytes) != 8) throw IOException("Unexpected EOF while reading float64 value.")
// Assemble 8 bytes into a 64-bit bit-pattern, then convert to Double
val bits = (bytes[7].toLong() and 0xFFL shl 56) or
(bytes[6].toLong() and 0xFFL shl 48) or
(bytes[5].toLong() and 0xFFL shl 40) or
(bytes[4].toLong() and 0xFFL shl 32) or
(bytes[3].toLong() and 0xFFL shl 24) or
(bytes[2].toLong() and 0xFFL shl 16) or
(bytes[1].toLong() and 0xFFL shl 8) or
(bytes[0].toLong() and 0xFFL)
val doubleVal = Double.fromBits(bits)
MetadataValue.Float64(doubleVal)
}
}
private fun <T> T?.takeUnless(check: T.() -> Boolean): T? =
this?.takeIf { !it.check() }
/** Helper: Skip a value in the stream without storing it (still maintains pointer). */
private fun skipValue(input: InputStream, type: MetadataType) {
when (type) {
MetadataType.UINT8, MetadataType.INT8, MetadataType.BOOL -> input.skipFully(1)
MetadataType.UINT16, MetadataType.INT16 -> input.skipFully(2)
MetadataType.UINT32, MetadataType.INT32, MetadataType.FLOAT32 -> input.skipFully(4)
MetadataType.UINT64, MetadataType.INT64, MetadataType.FLOAT64 -> input.skipFully(8)
MetadataType.STRING -> {
val len = readLittleLong(input); input.skipFully(len)
}
MetadataType.ARRAY -> {
val elemType = MetadataType.fromCode(littleEndianBytesToInt(input.readNBytesExact(4)))
val len = readLittleLong(input)
repeat(len.toInt()) { skipValue(input, elemType) } // recursive skip
}
}
}
/** Helper: Read an 8-byte little-endian unsigned value and return it as a signed Long (assuming it fits in 63 bits). */
private fun readLittleLong(input: InputStream): Long {
val bytes = ByteArray(8)
input.readFully(bytes)
// Combine 8 bytes into a 64-bit value (Little Endian).
// Note: If the value exceeds Long.MAX_VALUE (bit 63 is 1), this will produce a negative Long (two's complement).
// In our context (lengths/counts), such extremely large values are not expected.
return (bytes[7].toLong() and 0xFFL shl 56) or
(bytes[6].toLong() and 0xFFL shl 48) or
(bytes[5].toLong() and 0xFFL shl 40) or
(bytes[4].toLong() and 0xFFL shl 32) or
(bytes[3].toLong() and 0xFFL shl 24) or
(bytes[2].toLong() and 0xFFL shl 16) or
(bytes[1].toLong() and 0xFFL shl 8) or
(bytes[0].toLong() and 0xFFL)
}
/** Helper: Read a GGUF string from the stream (8-byte length followed by UTF-8 bytes). */
private fun readString(input: InputStream): String =
// Read 8-byte little-endian length (number of bytes in the string).
readLittleLong(input).let { len ->
if (len < 0 || len > Int.MAX_VALUE) throw IOException("String too long: $len")
// Read the UTF-8 bytes of the given length.
ByteArray(len.toInt()).let {
if (it.isNotEmpty()) input.readFully(it)
String(it, Charsets.UTF_8)
}
}
/** Helper: Convert a 4-byte little-endian byte array to a 32-bit integer. */
private fun littleEndianBytesToInt(bytes: ByteArray): Int =
// Note: assumes bytes length is 4.
(bytes[3].toInt() and 0xFF shl 24) or
(bytes[2].toInt() and 0xFF shl 16) or
(bytes[1].toInt() and 0xFF shl 8) or
(bytes[0].toInt() and 0xFF)
/**
* Robust skip that works the same on JDK 11 and Android’s desugared runtime.
*
* @param n Number of bytes to advance in the stream.
* @throws IOException on premature EOF.
*/
private fun InputStream.skipFully(n: Long) {
var remaining = n
val scratch = ByteArray(8192) // read‑and‑toss buffer
while (remaining > 0) {
val skipped = skip(remaining)
when {
skipped > 0 -> remaining -= skipped // normal fast path
skipped == 0L -> {
// fallback: read and discard
val read = read(scratch, 0, minOf(remaining, scratch.size.toLong()).toInt())
if (read == -1) throw IOException("EOF while skipping $n bytes")
remaining -= read
}
else -> throw IOException("Skip returned negative value")
}
}
}
/**
* Extension that keeps reading until the requested number of bytes are filled.
* Falls back to `read()` when `skip()` returns 0, which happens on some Android
* streams.
*
* @param buf Destination buffer.
* @param len Number of bytes to fill (defaults to `buf.size`).
* @throws IOException on premature EOF.
*/
private fun InputStream.readFully(buf: ByteArray, len: Int = buf.size) {
var off = 0
while (off < len) {
val n = read(buf, off, len - off)
if (n == -1) throw IOException("EOF after $off of $len bytes")
off += n
}
}
/**
* Read EXACTLY `n` bytes or throw – never returns a partially‑filled array.
* This is used for small fixed‑length reads (e.g. 4‑byte type codes).
*
* @throws IOException on premature EOF.
*/
private fun InputStream.readNBytesExact(n: Int) = ByteArray(n).also {
if (read(it) != n) throw IOException("Unexpected EOF")
}
}

0
examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt → examples/llama.android/lib/src/test/java/android/llama/cpp/ExampleUnitTest.kt
View File

71
examples/llama.android/llama/build.gradle.kts
View File

@ -1,71 +0,0 @@
plugins {
id("com.android.library")
id("org.jetbrains.kotlin.android")
}
android {
namespace = "android.llama.cpp"
compileSdk = 34
defaultConfig {
minSdk = 33
testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner"
consumerProguardFiles("consumer-rules.pro")
ndk {
// Add NDK properties if wanted, e.g.
// abiFilters += listOf("arm64-v8a")
}
externalNativeBuild {
cmake {
arguments += "-DLLAMA_CURL=OFF"
arguments += "-DLLAMA_BUILD_COMMON=ON"
arguments += "-DGGML_LLAMAFILE=OFF"
arguments += "-DCMAKE_BUILD_TYPE=Release"
cppFlags += listOf()
arguments += listOf()
cppFlags("")
}
}
}
buildTypes {
release {
isMinifyEnabled = false
proguardFiles(
getDefaultProguardFile("proguard-android-optimize.txt"),
"proguard-rules.pro"
)
}
}
externalNativeBuild {
cmake {
path("src/main/cpp/CMakeLists.txt")
version = "3.22.1"
}
}
compileOptions {
sourceCompatibility = JavaVersion.VERSION_1_8
targetCompatibility = JavaVersion.VERSION_1_8
}
kotlinOptions {
jvmTarget = "1.8"
}
packaging {
resources {
excludes += "/META-INF/{AL2.0,LGPL2.1}"
}
}
}
dependencies {
implementation("androidx.core:core-ktx:1.12.0")
implementation("androidx.appcompat:appcompat:1.6.1")
implementation("com.google.android.material:material:1.11.0")
testImplementation("junit:junit:4.13.2")
androidTestImplementation("androidx.test.ext:junit:1.1.5")
androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1")
}

0
examples/llama.android/llama/consumer-rules.pro
View File

53
examples/llama.android/llama/src/main/cpp/CMakeLists.txt
View File

@ -1,53 +0,0 @@
# For more information about using CMake with Android Studio, read the
# documentation: https://d.android.com/studio/projects/add-native-code.html.
# For more examples on how to use CMake, see https://github.com/android/ndk-samples.
# Sets the minimum CMake version required for this project.
cmake_minimum_required(VERSION 3.22.1)
# Declares the project name. The project name can be accessed via ${ PROJECT_NAME},
# Since this is the top level CMakeLists.txt, the project name is also accessible
# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level
# build script scope).
project("llama-android")
#include(FetchContent)
#FetchContent_Declare(
# llama
# GIT_REPOSITORY https://github.com/ggml-org/llama.cpp
# GIT_TAG master
#)
# Also provides "common"
#FetchContent_MakeAvailable(llama)
# Creates and names a library, sets it as either STATIC
# or SHARED, and provides the relative paths to its source code.
# You can define multiple libraries, and CMake builds them for you.
# Gradle automatically packages shared libraries with your APK.
#
# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define
# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME}
# is preferred for the same purpose.
#
#load local llama.cpp
add_subdirectory(../../../../../../ build-llama)
# In order to load a library into your app from Java/Kotlin, you must call
# System.loadLibrary() and pass the name of the library defined here;
# for GameActivity/NativeActivity derived applications, the same library name must be
# used in the AndroidManifest.xml file.
add_library(${CMAKE_PROJECT_NAME} SHARED
# List C/C++ source files with relative paths to this CMakeLists.txt.
llama-android.cpp)
# Specifies libraries CMake should link to your target library. You
# can link libraries from various origins, such as libraries defined in this
# build script, prebuilt third-party libraries, or Android system libraries.
target_link_libraries(${CMAKE_PROJECT_NAME}
# List libraries link to the target library
llama
common
android
log)

452
examples/llama.android/llama/src/main/cpp/llama-android.cpp
View File

@ -1,452 +0,0 @@
#include <android/log.h>
#include <jni.h>
#include <iomanip>
#include <math.h>
#include <string>
#include <unistd.h>
#include "llama.h"
#include "common.h"
// Write C++ code here.
//
// Do not forget to dynamically load the C++ library into your application.
//
// For instance,
//
// In MainActivity.java:
// static {
// System.loadLibrary("llama-android");
// }
//
// Or, in MainActivity.kt:
// companion object {
// init {
// System.loadLibrary("llama-android")
// }
// }
#define TAG "llama-android.cpp"
#define LOGi(...) __android_log_print(ANDROID_LOG_INFO, TAG, __VA_ARGS__)
#define LOGe(...) __android_log_print(ANDROID_LOG_ERROR, TAG, __VA_ARGS__)
jclass la_int_var;
jmethodID la_int_var_value;
jmethodID la_int_var_inc;
std::string cached_token_chars;
bool is_valid_utf8(const char * string) {
if (!string) {
return true;
}
const unsigned char * bytes = (const unsigned char *)string;
int num;
while (*bytes != 0x00) {
if ((*bytes & 0x80) == 0x00) {
// U+0000 to U+007F
num = 1;
} else if ((*bytes & 0xE0) == 0xC0) {
// U+0080 to U+07FF
num = 2;
} else if ((*bytes & 0xF0) == 0xE0) {
// U+0800 to U+FFFF
num = 3;
} else if ((*bytes & 0xF8) == 0xF0) {
// U+10000 to U+10FFFF
num = 4;
} else {
return false;
}
bytes += 1;
for (int i = 1; i < num; ++i) {
if ((*bytes & 0xC0) != 0x80) {
return false;
}
bytes += 1;
}
}
return true;
}
static void log_callback(ggml_log_level level, const char * fmt, void * data) {
if (level == GGML_LOG_LEVEL_ERROR) __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data);
else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data);
else if (level == GGML_LOG_LEVEL_WARN) __android_log_print(ANDROID_LOG_WARN, TAG, fmt, data);
else __android_log_print(ANDROID_LOG_DEFAULT, TAG, fmt, data);
}
extern "C"
JNIEXPORT jlong JNICALL
Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring filename) {
llama_model_params model_params = llama_model_default_params();
auto path_to_model = env->GetStringUTFChars(filename, 0);
LOGi("Loading model from %s", path_to_model);
auto model = llama_model_load_from_file(path_to_model, model_params);
env->ReleaseStringUTFChars(filename, path_to_model);
if (!model) {
LOGe("load_model() failed");
env->ThrowNew(env->FindClass("java/lang/IllegalStateException"), "load_model() failed");
return 0;
}
return reinterpret_cast<jlong>(model);
}
extern "C"
JNIEXPORT void JNICALL
Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) {
llama_model_free(reinterpret_cast<llama_model *>(model));
}
extern "C"
JNIEXPORT jlong JNICALL
Java_android_llama_cpp_LLamaAndroid_new_1context(JNIEnv *env, jobject, jlong jmodel) {
auto model = reinterpret_cast<llama_model *>(jmodel);
if (!model) {
LOGe("new_context(): model cannot be null");
env->ThrowNew(env->FindClass("java/lang/IllegalArgumentException"), "Model cannot be null");
return 0;
}
int n_threads = std::max(1, std::min(8, (int) sysconf(_SC_NPROCESSORS_ONLN) - 2));
LOGi("Using %d threads", n_threads);
llama_context_params ctx_params = llama_context_default_params();
ctx_params.n_ctx = 2048;
ctx_params.n_threads = n_threads;
ctx_params.n_threads_batch = n_threads;
llama_context * context = llama_new_context_with_model(model, ctx_params);
if (!context) {
LOGe("llama_new_context_with_model() returned null)");
env->ThrowNew(env->FindClass("java/lang/IllegalStateException"),
"llama_new_context_with_model() returned null)");
return 0;
}
return reinterpret_cast<jlong>(context);
}
extern "C"
JNIEXPORT void JNICALL
Java_android_llama_cpp_LLamaAndroid_free_1context(JNIEnv *, jobject, jlong context) {
llama_free(reinterpret_cast<llama_context *>(context));
}
extern "C"
JNIEXPORT void JNICALL
Java_android_llama_cpp_LLamaAndroid_backend_1free(JNIEnv *, jobject) {
llama_backend_free();
}
extern "C"
JNIEXPORT void JNICALL
Java_android_llama_cpp_LLamaAndroid_log_1to_1android(JNIEnv *, jobject) {
llama_log_set(log_callback, NULL);
}
extern "C"
JNIEXPORT jstring JNICALL
Java_android_llama_cpp_LLamaAndroid_bench_1model(
JNIEnv *env,
jobject,
jlong context_pointer,
jlong model_pointer,
jlong batch_pointer,
jint pp,
jint tg,
jint pl,
jint nr
) {
auto pp_avg = 0.0;
auto tg_avg = 0.0;
auto pp_std = 0.0;
auto tg_std = 0.0;
const auto context = reinterpret_cast<llama_context *>(context_pointer);
const auto model = reinterpret_cast<llama_model *>(model_pointer);
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
const int n_ctx = llama_n_ctx(context);
LOGi("n_ctx = %d", n_ctx);
int i, j;
int nri;
for (nri = 0; nri < nr; nri++) {
LOGi("Benchmark prompt processing (pp)");
common_batch_clear(*batch);
const int n_tokens = pp;
for (i = 0; i < n_tokens; i++) {
common_batch_add(*batch, 0, i, { 0 }, false);
}
batch->logits[batch->n_tokens - 1] = true;
llama_memory_clear(llama_get_memory(context), false);
const auto t_pp_start = ggml_time_us();
if (llama_decode(context, *batch) != 0) {
LOGi("llama_decode() failed during prompt processing");
}
const auto t_pp_end = ggml_time_us();
// bench text generation
LOGi("Benchmark text generation (tg)");
llama_memory_clear(llama_get_memory(context), false);
const auto t_tg_start = ggml_time_us();
for (i = 0; i < tg; i++) {
common_batch_clear(*batch);
for (j = 0; j < pl; j++) {
common_batch_add(*batch, 0, i, { j }, true);
}
LOGi("llama_decode() text generation: %d", i);
if (llama_decode(context, *batch) != 0) {
LOGi("llama_decode() failed during text generation");
}
}
const auto t_tg_end = ggml_time_us();
llama_memory_clear(llama_get_memory(context), false);
const auto t_pp = double(t_pp_end - t_pp_start) / 1000000.0;
const auto t_tg = double(t_tg_end - t_tg_start) / 1000000.0;
const auto speed_pp = double(pp) / t_pp;
const auto speed_tg = double(pl * tg) / t_tg;
pp_avg += speed_pp;
tg_avg += speed_tg;
pp_std += speed_pp * speed_pp;
tg_std += speed_tg * speed_tg;
LOGi("pp %f t/s, tg %f t/s", speed_pp, speed_tg);
}
pp_avg /= double(nr);
tg_avg /= double(nr);
if (nr > 1) {
pp_std = sqrt(pp_std / double(nr - 1) - pp_avg * pp_avg * double(nr) / double(nr - 1));
tg_std = sqrt(tg_std / double(nr - 1) - tg_avg * tg_avg * double(nr) / double(nr - 1));
} else {
pp_std = 0;
tg_std = 0;
}
char model_desc[128];
llama_model_desc(model, model_desc, sizeof(model_desc));
const auto model_size = double(llama_model_size(model)) / 1024.0 / 1024.0 / 1024.0;
const auto model_n_params = double(llama_model_n_params(model)) / 1e9;
const auto backend = "(Android)"; // TODO: What should this be?
std::stringstream result;
result << std::setprecision(2);
result << "| model | size | params | backend | test | t/s |\n";
result << "| --- | --- | --- | --- | --- | --- |\n";
result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | pp " << pp << " | " << pp_avg << " ± " << pp_std << " |\n";
result << "| " << model_desc << " | " << model_size << "GiB | " << model_n_params << "B | " << backend << " | tg " << tg << " | " << tg_avg << " ± " << tg_std << " |\n";
return env->NewStringUTF(result.str().c_str());
}
extern "C"
JNIEXPORT jlong JNICALL
Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) {
// Source: Copy of llama.cpp:llama_batch_init but heap-allocated.
llama_batch *batch = new llama_batch {
0,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
nullptr,
};
if (embd) {
batch->embd = (float *) malloc(sizeof(float) * n_tokens * embd);
} else {
batch->token = (llama_token *) malloc(sizeof(llama_token) * n_tokens);
}
batch->pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens);
batch->n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens);
batch->seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens);
for (int i = 0; i < n_tokens; ++i) {
batch->seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max);
}
batch->logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens);
return reinterpret_cast<jlong>(batch);
}
extern "C"
JNIEXPORT void JNICALL
Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) {
//llama_batch_free(*reinterpret_cast<llama_batch *>(batch_pointer));
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
delete batch;
}
extern "C"
JNIEXPORT jlong JNICALL
Java_android_llama_cpp_LLamaAndroid_new_1sampler(JNIEnv *, jobject) {
auto sparams = llama_sampler_chain_default_params();
sparams.no_perf = true;
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
return reinterpret_cast<jlong>(smpl);
}
extern "C"
JNIEXPORT void JNICALL
Java_android_llama_cpp_LLamaAndroid_free_1sampler(JNIEnv *, jobject, jlong sampler_pointer) {
llama_sampler_free(reinterpret_cast<llama_sampler *>(sampler_pointer));
}
extern "C"
JNIEXPORT void JNICALL
Java_android_llama_cpp_LLamaAndroid_backend_1init(JNIEnv *, jobject) {
llama_backend_init();
}
extern "C"
JNIEXPORT jstring JNICALL
Java_android_llama_cpp_LLamaAndroid_system_1info(JNIEnv *env, jobject) {
return env->NewStringUTF(llama_print_system_info());
}
extern "C"
JNIEXPORT jint JNICALL
Java_android_llama_cpp_LLamaAndroid_completion_1init(
JNIEnv *env,
jobject,
jlong context_pointer,
jlong batch_pointer,
jstring jtext,
jboolean format_chat,
jint n_len
) {
cached_token_chars.clear();
const auto text = env->GetStringUTFChars(jtext, 0);
const auto context = reinterpret_cast<llama_context *>(context_pointer);
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
bool parse_special = (format_chat == JNI_TRUE);
const auto tokens_list = common_tokenize(context, text, true, parse_special);
auto n_ctx = llama_n_ctx(context);
auto n_kv_req = tokens_list.size() + n_len;
LOGi("n_len = %d, n_ctx = %d, n_kv_req = %d", n_len, n_ctx, n_kv_req);
if (n_kv_req > n_ctx) {
LOGe("error: n_kv_req > n_ctx, the required KV cache size is not big enough");
}
for (auto id : tokens_list) {
LOGi("token: `%s`-> %d ", common_token_to_piece(context, id).c_str(), id);
}
common_batch_clear(*batch);
// evaluate the initial prompt
for (auto i = 0; i < tokens_list.size(); i++) {
common_batch_add(*batch, tokens_list[i], i, { 0 }, false);
}
// llama_decode will output logits only for the last token of the prompt
batch->logits[batch->n_tokens - 1] = true;
if (llama_decode(context, *batch) != 0) {
LOGe("llama_decode() failed");
}
env->ReleaseStringUTFChars(jtext, text);
return batch->n_tokens;
}
extern "C"
JNIEXPORT jstring JNICALL
Java_android_llama_cpp_LLamaAndroid_completion_1loop(
JNIEnv * env,
jobject,
jlong context_pointer,
jlong batch_pointer,
jlong sampler_pointer,
jint n_len,
jobject intvar_ncur
) {
const auto context = reinterpret_cast<llama_context *>(context_pointer);
const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
const auto sampler = reinterpret_cast<llama_sampler *>(sampler_pointer);
const auto model = llama_get_model(context);
const auto vocab = llama_model_get_vocab(model);
if (!la_int_var) la_int_var = env->GetObjectClass(intvar_ncur);
if (!la_int_var_value) la_int_var_value = env->GetMethodID(la_int_var, "getValue", "()I");
if (!la_int_var_inc) la_int_var_inc = env->GetMethodID(la_int_var, "inc", "()V");
// sample the most likely token
const auto new_token_id = llama_sampler_sample(sampler, context, -1);
const auto n_cur = env->CallIntMethod(intvar_ncur, la_int_var_value);
if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_len) {
return nullptr;
}
auto new_token_chars = common_token_to_piece(context, new_token_id);
cached_token_chars += new_token_chars;
jstring new_token = nullptr;
if (is_valid_utf8(cached_token_chars.c_str())) {
new_token = env->NewStringUTF(cached_token_chars.c_str());
LOGi("cached: %s, new_token_chars: `%s`, id: %d", cached_token_chars.c_str(), new_token_chars.c_str(), new_token_id);
cached_token_chars.clear();
} else {
new_token = env->NewStringUTF("");
}
common_batch_clear(*batch);
common_batch_add(*batch, new_token_id, n_cur, { 0 }, true);
env->CallVoidMethod(intvar_ncur, la_int_var_inc);
if (llama_decode(context, *batch) != 0) {
LOGe("llama_decode() returned null");
}
return new_token;
}
extern "C"
JNIEXPORT void JNICALL
Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) {
llama_memory_clear(llama_get_memory(reinterpret_cast<llama_context *>(context)), true);
}

180
examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt
View File

@ -1,180 +0,0 @@
package android.llama.cpp
import android.util.Log
import kotlinx.coroutines.CoroutineDispatcher
import kotlinx.coroutines.asCoroutineDispatcher
import kotlinx.coroutines.flow.Flow
import kotlinx.coroutines.flow.flow
import kotlinx.coroutines.flow.flowOn
import kotlinx.coroutines.withContext
import java.util.concurrent.Executors
import kotlin.concurrent.thread
class LLamaAndroid {
private val tag: String? = this::class.simpleName
private val threadLocalState: ThreadLocal<State> = ThreadLocal.withInitial { State.Idle }
private val runLoop: CoroutineDispatcher = Executors.newSingleThreadExecutor {
thread(start = false, name = "Llm-RunLoop") {
Log.d(tag, "Dedicated thread for native code: ${Thread.currentThread().name}")
// No-op if called more than once.
System.loadLibrary("llama-android")
// Set llama log handler to Android
log_to_android()
backend_init(false)
Log.d(tag, system_info())
it.run()
}.apply {
uncaughtExceptionHandler = Thread.UncaughtExceptionHandler { _, exception: Throwable ->
Log.e(tag, "Unhandled exception", exception)
}
}
}.asCoroutineDispatcher()
private val nlen: Int = 64
private external fun log_to_android()
private external fun load_model(filename: String): Long
private external fun free_model(model: Long)
private external fun new_context(model: Long): Long
private external fun free_context(context: Long)
private external fun backend_init(numa: Boolean)
private external fun backend_free()
private external fun new_batch(nTokens: Int, embd: Int, nSeqMax: Int): Long
private external fun free_batch(batch: Long)
private external fun new_sampler(): Long
private external fun free_sampler(sampler: Long)
private external fun bench_model(
context: Long,
model: Long,
batch: Long,
pp: Int,
tg: Int,
pl: Int,
nr: Int
): String
private external fun system_info(): String
private external fun completion_init(
context: Long,
batch: Long,
text: String,
formatChat: Boolean,
nLen: Int
): Int
private external fun completion_loop(
context: Long,
batch: Long,
sampler: Long,
nLen: Int,
ncur: IntVar
): String?
private external fun kv_cache_clear(context: Long)
suspend fun bench(pp: Int, tg: Int, pl: Int, nr: Int = 1): String {
return withContext(runLoop) {
when (val state = threadLocalState.get()) {
is State.Loaded -> {
Log.d(tag, "bench(): $state")
bench_model(state.context, state.model, state.batch, pp, tg, pl, nr)
}
else -> throw IllegalStateException("No model loaded")
}
}
}
suspend fun load(pathToModel: String) {
withContext(runLoop) {
when (threadLocalState.get()) {
is State.Idle -> {
val model = load_model(pathToModel)
if (model == 0L) throw IllegalStateException("load_model() failed")
val context = new_context(model)
if (context == 0L) throw IllegalStateException("new_context() failed")
val batch = new_batch(512, 0, 1)
if (batch == 0L) throw IllegalStateException("new_batch() failed")
val sampler = new_sampler()
if (sampler == 0L) throw IllegalStateException("new_sampler() failed")
Log.i(tag, "Loaded model $pathToModel")
threadLocalState.set(State.Loaded(model, context, batch, sampler))
}
else -> throw IllegalStateException("Model already loaded")
}
}
}
fun send(message: String, formatChat: Boolean = false): Flow<String> = flow {
when (val state = threadLocalState.get()) {
is State.Loaded -> {
val ncur = IntVar(completion_init(state.context, state.batch, message, formatChat, nlen))
while (ncur.value <= nlen) {
val str = completion_loop(state.context, state.batch, state.sampler, nlen, ncur)
if (str == null) {
break
}
emit(str)
}
kv_cache_clear(state.context)
}
else -> {}
}
}.flowOn(runLoop)
/**
* Unloads the model and frees resources.
*
* This is a no-op if there's no model loaded.
*/
suspend fun unload() {
withContext(runLoop) {
when (val state = threadLocalState.get()) {
is State.Loaded -> {
free_context(state.context)
free_model(state.model)
free_batch(state.batch)
free_sampler(state.sampler);
threadLocalState.set(State.Idle)
}
else -> {}
}
}
}
companion object {
private class IntVar(value: Int) {
@Volatile
var value: Int = value
private set
fun inc() {
synchronized(this) {
value += 1
}
}
}
private sealed interface State {
data object Idle: State
data class Loaded(val model: Long, val context: Long, val batch: Long, val sampler: Long): State
}
// Enforce only one instance of Llm.
private val _instance: LLamaAndroid = LLamaAndroid()
fun instance(): LLamaAndroid = _instance
}
}

6
examples/llama.android/settings.gradle.kts
View File

@ -8,11 +8,11 @@ pluginManagement {
dependencyResolutionManagement {
repositoriesMode.set(RepositoriesMode.FAIL_ON_PROJECT_REPOS)
repositories {
google()
mavenCentral()
google()
}
}
rootProject.name = "LlamaAndroid"
rootProject.name = "AiChat"
include(":app")
include(":llama")
include(":lib")

176
examples/model-conversion/scripts/causal/run-org-model.py
View File

@ -2,134 +2,22 @@
import argparse
import os
import sys
import importlib
from pathlib import Path
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
# Add parent directory to path for imports
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForImageTextToText, AutoConfig
import torch
import numpy as np
### If you want to dump RoPE activations, apply this monkey patch to the model
### class from Transformers that you are running (replace apertus.modeling_apertus
### with the proper package and class for your model
### === START ROPE DEBUG ===
# from transformers.models.apertus.modeling_apertus import apply_rotary_pos_emb
# orig_rope = apply_rotary_pos_emb
# torch.set_printoptions(threshold=float('inf'))
# torch.set_printoptions(precision=6, sci_mode=False)
# def debug_rope(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
# # log inputs
# summarize(q, "RoPE.q_in")
# summarize(k, "RoPE.k_in")
# # call original
# q_out, k_out = orig_rope(q, k, cos, sin, position_ids, unsqueeze_dim)
# # log outputs
# summarize(q_out, "RoPE.q_out")
# summarize(k_out, "RoPE.k_out")
# return q_out, k_out
# # Patch it
# import transformers.models.apertus.modeling_apertus as apertus_mod # noqa: E402
# apertus_mod.apply_rotary_pos_emb = debug_rope
### == END ROPE DEBUG ===
def summarize(tensor: torch.Tensor, name: str, max_seq: int = 3, max_vals: int = 3):
"""
Print a tensor in llama.cpp debug style.
Supports:
- 2D tensors (seq, hidden)
- 3D tensors (batch, seq, hidden)
- 4D tensors (batch, seq, heads, dim_per_head) via flattening heads × dim_per_head
Shows first and last max_vals of each vector per sequence position.
"""
t = tensor.detach().to(torch.float32).cpu()
# Determine dimensions
if t.ndim == 3:
_, s, _ = t.shape
elif t.ndim == 2:
_, s = 1, t.shape[0]
t = t.unsqueeze(0)
elif t.ndim == 4:
_, s, _, _ = t.shape
else:
print(f"Skipping tensor due to unsupported dimensions: {t.ndim}")
return
ten_shape = t.shape
print(f"ggml_debug: {name} = (f32) ... = {{{ten_shape}}}")
print(" [")
print(" [")
# Determine indices for first and last sequences
first_indices = list(range(min(s, max_seq)))
last_indices = list(range(max(0, s - max_seq), s))
# Check if there's an overlap between first and last indices or if we're at the edge case of s = 2 * max_seq
has_overlap = bool(set(first_indices) & set(last_indices)) or (max_seq * 2 == s)
# Combine indices
if has_overlap:
# If there's overlap, just use the combined unique indices
indices = sorted(list(set(first_indices + last_indices)))
separator_index = None
else:
# If no overlap, we'll add a separator between first and last sequences
indices = first_indices + last_indices
separator_index = len(first_indices)
for i, si in enumerate(indices):
# Add separator if needed
if separator_index is not None and i == separator_index:
print(" ...")
# Extract appropriate slice
vec = t[0, si]
if vec.ndim == 2: # 4D case: flatten heads × dim_per_head
flat = vec.flatten().tolist()
else: # 2D or 3D case
flat = vec.tolist()
# First and last slices
first = flat[:max_vals]
last = flat[-max_vals:] if len(flat) >= max_vals else flat
first_str = ", ".join(f"{v:12.4f}" for v in first)
last_str = ", ".join(f"{v:12.4f}" for v in last)
print(f" [{first_str}, ..., {last_str}]")
print(" ],")
print(" ]")
print(f" sum = {t.sum().item():.6f}\n")
def debug_hook(name):
def fn(_m, input, output):
if isinstance(input, torch.Tensor):
summarize(input, name + "_in")
elif isinstance(input, (tuple, list)) and isinstance(input[0], torch.Tensor):
summarize(input[0], name + "_in")
if isinstance(output, torch.Tensor):
summarize(output, name + "_out")
elif isinstance(output, (tuple, list)) and isinstance(output[0], torch.Tensor):
summarize(output[0], name + "_out")
return fn
unreleased_model_name = os.getenv("UNRELEASED_MODEL_NAME")
from utils.common import debug_hook
parser = argparse.ArgumentParser(description="Process model with specified path")
parser.add_argument("--model-path", "-m", help="Path to the model")
parser.add_argument("--prompt-file", "-f", help="Optional prompt file", required=False)
parser.add_argument("--verbose", "-v", action="store_true", help="Enable verbose debug output")
args = parser.parse_args()
model_path = os.environ.get("MODEL_PATH", args.model_path)
@ -138,18 +26,30 @@ if model_path is None:
"Model path must be specified either via --model-path argument or MODEL_PATH environment variable"
)
### If you want to dump RoPE activations, uncomment the following lines:
### === START ROPE DEBUG ===
# from utils.common import setup_rope_debug
# setup_rope_debug("transformers.models.apertus.modeling_apertus")
### == END ROPE DEBUG ===
print("Loading model and tokenizer using AutoTokenizer:", model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
multimodal = False
full_config = config
print("Model type: ", config.model_type)
if "vocab_size" not in config and "text_config" in config:
config = config.text_config
multimodal = True
print("Vocab size: ", config.vocab_size)
print("Hidden size: ", config.hidden_size)
print("Number of layers: ", config.num_hidden_layers)
print("BOS token id: ", config.bos_token_id)
print("EOS token id: ", config.eos_token_id)
unreleased_model_name = os.getenv("UNRELEASED_MODEL_NAME")
if unreleased_model_name:
model_name_lower = unreleased_model_name.lower()
unreleased_module_path = (
@ -169,13 +69,19 @@ if unreleased_model_name:
print(f"Failed to import or load model: {e}")
exit(1)
else:
model = AutoModelForCausalLM.from_pretrained(
model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=config
)
if multimodal:
model = AutoModelForImageTextToText.from_pretrained(
model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=full_config
)
else:
model = AutoModelForCausalLM.from_pretrained(
model_path, device_map="auto", offload_folder="offload", trust_remote_code=True, config=config
)
for name, module in model.named_modules():
if len(list(module.children())) == 0: # only leaf modules
module.register_forward_hook(debug_hook(name))
if args.verbose:
for name, module in model.named_modules():
if len(list(module.children())) == 0: # only leaf modules
module.register_forward_hook(debug_hook(name))
model_name = os.path.basename(model_path)
# Printing the Model class to allow for easier debugging. This can be useful
@ -185,7 +91,10 @@ model_name = os.path.basename(model_path)
print(f"Model class: {model.__class__.__name__}")
device = next(model.parameters()).device
if os.getenv("MODEL_TESTING_PROMPT"):
if args.prompt_file:
with open(args.prompt_file, encoding='utf-8') as f:
prompt = f.read()
elif os.getenv("MODEL_TESTING_PROMPT"):
prompt = os.getenv("MODEL_TESTING_PROMPT")
else:
prompt = "Hello, my name is"
@ -195,9 +104,18 @@ print(f"Input tokens: {input_ids}")
print(f"Input text: {repr(prompt)}")
print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
batch_size = 512
with torch.no_grad():
outputs = model(input_ids.to(model.device))
logits = outputs.logits
past = None
outputs = None
for i in range(0, input_ids.size(1), batch_size):
print(f"Processing chunk with tokens {i} to {i + batch_size}")
chunk = input_ids[:, i:i + batch_size]
outputs = model(chunk.to(model.device), past_key_values=past, use_cache=True)
past = outputs.past_key_values
logits = outputs.logits # type: ignore
# Extract logits for the last token (next token prediction)
last_logits = logits[0, -1, :].float().cpu().numpy()

130
examples/model-conversion/scripts/utils/common.py
View File

@ -2,6 +2,8 @@
import os
import sys
import torch
def get_model_name_from_env_path(env_path_name):
model_path = os.getenv(env_path_name)
@ -18,3 +20,131 @@ def get_model_name_from_env_path(env_path_name):
name = name[:-5]
return name
def summarize(tensor: torch.Tensor, name: str, max_seq: int = 3, max_vals: int = 3):
"""
Print a tensor in llama.cpp debug style.
Supports:
- 2D tensors (seq, hidden)
- 3D tensors (batch, seq, hidden)
- 4D tensors (batch, seq, heads, dim_per_head) via flattening heads × dim_per_head
Shows first and last max_vals of each vector per sequence position.
"""
t = tensor.detach().to(torch.float32).cpu()
# Determine dimensions
if t.ndim == 3:
_, s, _ = t.shape
elif t.ndim == 2:
_, s = 1, t.shape[0]
t = t.unsqueeze(0)
elif t.ndim == 4:
_, s, _, _ = t.shape
else:
print(f"Skipping tensor due to unsupported dimensions: {t.ndim}")
return
ten_shape = t.shape
print(f"ggml_debug: {name} = (f32) ... = {{{ten_shape}}}")
print(" [")
print(" [")
# Determine indices for first and last sequences
first_indices = list(range(min(s, max_seq)))
last_indices = list(range(max(0, s - max_seq), s))
# Check if there's an overlap between first and last indices or if we're at the edge case of s = 2 * max_seq
has_overlap = bool(set(first_indices) & set(last_indices)) or (max_seq * 2 == s)
# Combine indices
if has_overlap:
# If there's overlap, just use the combined unique indices
indices = sorted(list(set(first_indices + last_indices)))
separator_index = None
else:
# If no overlap, we'll add a separator between first and last sequences
indices = first_indices + last_indices
separator_index = len(first_indices)
for i, si in enumerate(indices):
# Add separator if needed
if separator_index is not None and i == separator_index:
print(" ...")
# Extract appropriate slice
vec = t[0, si]
if vec.ndim == 2: # 4D case: flatten heads × dim_per_head
flat = vec.flatten().tolist()
else: # 2D or 3D case
flat = vec.tolist()
# First and last slices
first = flat[:max_vals]
last = flat[-max_vals:] if len(flat) >= max_vals else flat
first_str = ", ".join(f"{v:12.4f}" for v in first)
last_str = ", ".join(f"{v:12.4f}" for v in last)
print(f" [{first_str}, ..., {last_str}]")
print(" ],")
print(" ]")
print(f" sum = {t.sum().item():.6f}\n")
def debug_hook(name):
def fn(_m, input, output):
if isinstance(input, torch.Tensor):
summarize(input, name + "_in")
elif isinstance(input, (tuple, list)) and len(input) > 0 and isinstance(input[0], torch.Tensor):
summarize(input[0], name + "_in")
if isinstance(output, torch.Tensor):
summarize(output, name + "_out")
elif isinstance(output, (tuple, list)) and len(output) > 0 and isinstance(output[0], torch.Tensor):
summarize(output[0], name + "_out")
return fn
def setup_rope_debug(model_module_path: str, function_name: str = "apply_rotary_pos_emb"):
"""
Apply monkey patch to dump RoPE activations for debugging.
Args:
model_module_path: Path to the model module (e.g., "transformers.models.apertus.modeling_apertus")
function_name: Name of the RoPE function to patch (default: "apply_rotary_pos_emb")
Example:
from utils.common import setup_rope_debug
setup_rope_debug("transformers.models.apertus.modeling_apertus")
"""
import importlib
# Import the module and get the original function
module = importlib.import_module(model_module_path)
orig_rope = getattr(module, function_name)
# Set torch print options for better debugging
torch.set_printoptions(threshold=float('inf'))
torch.set_printoptions(precision=6, sci_mode=False)
def debug_rope(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
# log inputs
summarize(q, "RoPE.q_in")
summarize(k, "RoPE.k_in")
# call original
q_out, k_out = orig_rope(q, k, cos, sin, position_ids, unsqueeze_dim)
# log outputs
summarize(q_out, "RoPE.q_out")
summarize(k_out, "RoPE.k_out")
return q_out, k_out
# Patch it
setattr(module, function_name, debug_rope)
print(f"RoPE debug patching applied to {model_module_path}.{function_name}")

4
examples/speculative/speculative.cpp
View File

@ -242,7 +242,7 @@ int main(int argc, char ** argv) {
bool accept = false;
if (params.sampling.temp > 0) {
// stochastic verification
common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]);
common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
auto & dist_tgt = *common_sampler_get_candidates(smpl, true);
@ -491,7 +491,7 @@ int main(int argc, char ** argv) {
continue;
}
common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft);
common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl, true);

1
ggml/CMakeLists.txt
View File

@ -254,6 +254,7 @@ set (GGML_OPENCL_TARGET_VERSION "300" CACHE STRING
"gmml: OpenCL API version to target")
option(GGML_HEXAGON "ggml: enable Hexagon backend" OFF)
set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml: quantize group size (32, 64, or 128)")
# toolchain for vulkan-shaders-gen
set (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "" CACHE FILEPATH "ggml: toolchain file for vulkan-shaders-gen")

3
ggml/src/CMakeLists.txt
View File

@ -386,6 +386,9 @@ if (GGML_CPU_ALL_VARIANTS)
ggml_add_cpu_backend_variant(android_armv8.2_1 DOTPROD)
ggml_add_cpu_backend_variant(android_armv8.2_2 DOTPROD FP16_VECTOR_ARITHMETIC)
ggml_add_cpu_backend_variant(android_armv8.6_1 DOTPROD FP16_VECTOR_ARITHMETIC MATMUL_INT8)
ggml_add_cpu_backend_variant(android_armv9.0_1 DOTPROD MATMUL_INT8 FP16_VECTOR_ARITHMETIC SVE2)
ggml_add_cpu_backend_variant(android_armv9.2_1 DOTPROD MATMUL_INT8 FP16_VECTOR_ARITHMETIC SME)
ggml_add_cpu_backend_variant(android_armv9.2_2 DOTPROD MATMUL_INT8 FP16_VECTOR_ARITHMETIC SVE SME)
elseif (APPLE)
ggml_add_cpu_backend_variant(apple_m1 DOTPROD)
ggml_add_cpu_backend_variant(apple_m2_m3 DOTPROD MATMUL_INT8)

4
ggml/src/ggml-cpu/CMakeLists.txt
View File

@ -458,6 +458,7 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
if (GGML_RV_ZFH)
string(APPEND MARCH_STR "_zfh")
endif()
if (GGML_XTHEADVECTOR)
string(APPEND MARCH_STR "_xtheadvector")
elseif (GGML_RVV)
@ -465,6 +466,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
if (GGML_RV_ZVFH)
string(APPEND MARCH_STR "_zvfh")
endif()
if (GGML_RV_ZVFBFWMA)
string(APPEND MARCH_STR "_zvfbfwma")
endif()
endif()
if (GGML_RV_ZICBOP)
string(APPEND MARCH_STR "_zicbop")

28
ggml/src/ggml-cpu/arch-fallback.h
View File

@ -43,6 +43,8 @@
#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
#define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
#define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
@ -51,6 +53,8 @@
#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
#define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
#define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
#elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64)
// repack.cpp
#define ggml_quantize_mat_q8_K_4x4_generic ggml_quantize_mat_q8_K_4x4
@ -67,10 +71,14 @@
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
#define ggml_gemv_q4_K_8x4_q8_K_generic ggml_gemv_q4_K_8x4_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
#define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
#define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
#elif defined(__POWERPC__) || defined(__powerpc__)
// ref: https://github.com/ggml-org/llama.cpp/pull/14146#issuecomment-2972561679
// quants.c
@ -91,6 +99,8 @@
#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
#define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
#define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
@ -99,6 +109,8 @@
#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
#define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
#define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
#elif defined(__loongarch64)
// quants.c
#define quantize_row_q8_K_generic quantize_row_q8_K
@ -119,6 +131,8 @@
#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
#define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
#define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
@ -127,6 +141,8 @@
#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
#define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
#define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
#elif defined(__riscv)
// quants.c
#define quantize_row_q8_K_generic quantize_row_q8_K
@ -154,6 +170,8 @@
#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
#define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
#define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_K_8x4_q8_K_generic ggml_gemm_q4_K_8x4_q8_K
@ -161,6 +179,8 @@
#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
#define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
#define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
#elif defined(__s390x__)
// quants.c
#define quantize_row_q8_K_generic quantize_row_q8_K
@ -187,6 +207,8 @@
#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
#define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
#define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
@ -195,6 +217,8 @@
#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
#define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
#define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
#elif defined(__wasm__)
// quants.c
#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
@ -223,6 +247,8 @@
#define ggml_gemv_q2_K_8x8_q8_K_generic ggml_gemv_q2_K_8x8_q8_K
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
#define ggml_gemv_iq4_nl_8x8_q8_0_generic ggml_gemv_iq4_nl_8x8_q8_0
#define ggml_gemv_q8_0_4x4_q8_0_generic ggml_gemv_q8_0_4x4_q8_0
#define ggml_gemv_q8_0_4x8_q8_0_generic ggml_gemv_q8_0_4x8_q8_0
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
@ -231,4 +257,6 @@
#define ggml_gemm_q2_K_8x8_q8_K_generic ggml_gemm_q2_K_8x8_q8_K
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
#define ggml_gemm_iq4_nl_8x8_q8_0_generic ggml_gemm_iq4_nl_8x8_q8_0
#define ggml_gemm_q8_0_4x4_q8_0_generic ggml_gemm_q8_0_4x4_q8_0
#define ggml_gemm_q8_0_4x8_q8_0_generic ggml_gemm_q8_0_4x8_q8_0
#endif

283
ggml/src/ggml-cpu/arch/arm/repack.cpp
View File

@ -786,6 +786,133 @@ void ggml_gemv_q4_K_8x8_q8_K(int n,
ggml_gemv_q4_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc);
}
void ggml_gemv_q8_0_4x4_q8_0(int n,
float * GGML_RESTRICT s,
size_t bs,
const void * GGML_RESTRICT vx,
const void * GGML_RESTRICT vy,
int nr,
int nc) {
const int qk = QK8_0;
const int nb = n / qk;
const int ncols_interleaved = 4;
const int blocklen = 4;
assert(n % qk == 0);
assert(nc % ncols_interleaved == 0);
UNUSED(nb);
UNUSED(ncols_interleaved);
UNUSED(blocklen);
#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
const block_q8_0x4 * b_ptr = (const block_q8_0x4 *) vx;
for (int c = 0; c < nc; c += ncols_interleaved) {
const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
float32x4_t acc = vdupq_n_f32(0);
for (int b = 0; b < nb; b++) {
int8x16x4_t b_low = vld1q_s8_x4((const int8_t *) b_ptr->qs);
int8x16x4_t b_high = vld1q_s8_x4((const int8_t *) b_ptr->qs + 64);
float16x4_t bd = vld1_f16((const __fp16 *) b_ptr->d);
int8x16x2_t a = vld1q_s8_x2(a_ptr->qs);
float16x4_t ad = vld1_dup_f16((const __fp16 *) &a_ptr->d);
int32x4_t ret = vdupq_n_s32(0);
ret = vdotq_laneq_s32(ret, b_low.val[0], a.val[0], 0);
ret = vdotq_laneq_s32(ret, b_low.val[1], a.val[0], 1);
ret = vdotq_laneq_s32(ret, b_low.val[2], a.val[0], 2);
ret = vdotq_laneq_s32(ret, b_low.val[3], a.val[0], 3);
ret = vdotq_laneq_s32(ret, b_high.val[0], a.val[1], 0);
ret = vdotq_laneq_s32(ret, b_high.val[1], a.val[1], 1);
ret = vdotq_laneq_s32(ret, b_high.val[2], a.val[1], 2);
ret = vdotq_laneq_s32(ret, b_high.val[3], a.val[1], 3);
acc = vfmaq_f32(acc, vcvtq_f32_s32(ret), vmulq_f32(vcvt_f32_f16(ad), vcvt_f32_f16(bd)));
a_ptr++;
b_ptr++;
}
vst1q_f32(s, acc);
s += ncols_interleaved;
}
return;
#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
ggml_gemv_q8_0_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
}
void ggml_gemv_q8_0_4x8_q8_0(int n,
float * GGML_RESTRICT s,
size_t bs,
const void * GGML_RESTRICT vx,
const void * GGML_RESTRICT vy,
int nr,
int nc) {
const int qk = QK8_0;
const int nb = n / qk;
const int ncols_interleaved = 4;
const int blocklen = 8;
assert(n % qk == 0);
assert(nc % ncols_interleaved == 0);
UNUSED(nb);
UNUSED(ncols_interleaved);
UNUSED(blocklen);
#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
const block_q8_0x4 * b_ptr = (const block_q8_0x4 *) vx;
for (int c = 0; c < nc; c += ncols_interleaved) {
const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
float32x4_t acc = vdupq_n_f32(0);
for (int b = 0; b < nb; b++) {
int8x16x4_t b_low = vld1q_s8_x4((const int8_t *) b_ptr->qs);
int8x16x4_t b_high = vld1q_s8_x4((const int8_t *) b_ptr->qs + 64);
float16x4_t bd = vld1_f16((const __fp16 *) b_ptr->d);
int8x8x4_t a_chunks = vld1_s8_x4(a_ptr->qs);
int8x16_t a0 = vcombine_s8(a_chunks.val[0], a_chunks.val[0]);
int8x16_t a1 = vcombine_s8(a_chunks.val[1], a_chunks.val[1]);
int8x16_t a2 = vcombine_s8(a_chunks.val[2], a_chunks.val[2]);
int8x16_t a3 = vcombine_s8(a_chunks.val[3], a_chunks.val[3]);
float16x4_t ad = vld1_dup_f16((const __fp16 *) &a_ptr->d);
int32x4_t ret0 = vdupq_n_s32(0);
int32x4_t ret1 = vdupq_n_s32(0);
// 0..7
ret0 = vdotq_s32(ret0, b_low.val[0], a0);
ret1 = vdotq_s32(ret1, b_low.val[1], a0);
// 8..15
ret0 = vdotq_s32(ret0, b_low.val[2], a1);
ret1 = vdotq_s32(ret1, b_low.val[3], a1);
// 16..23
ret0 = vdotq_s32(ret0, b_high.val[0], a2);
ret1 = vdotq_s32(ret1, b_high.val[1], a2);
// 24..31
ret0 = vdotq_s32(ret0, b_high.val[2], a3);
ret1 = vdotq_s32(ret1, b_high.val[3], a3);
int32x4_t ret = vpaddq_s32(ret0, ret1);
acc = vfmaq_f32(acc, vcvtq_f32_s32(ret), vmulq_f32(vcvt_f32_f16(ad), vcvt_f32_f16(bd)));
a_ptr++;
b_ptr++;
}
vst1q_f32(s, acc);
s += ncols_interleaved;
}
return;
#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
ggml_gemv_q8_0_4x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
}
void ggml_gemm_q4_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK8_0;
const int nb = n / qk;
@ -2610,3 +2737,159 @@ void ggml_gemm_q4_K_8x8_q8_K(int n,
#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
ggml_gemm_q4_K_8x8_q8_K_generic(n, s, bs, vx, vy, nr, nc);
}
void ggml_gemm_q8_0_4x4_q8_0(int n,
float * GGML_RESTRICT s,
size_t bs,
const void * GGML_RESTRICT vx,
const void * GGML_RESTRICT vy,
int nr,
int nc) {
const int qk = QK8_0;
const int nb = n / qk;
const int ncols_interleaved = 4;
const int blocklen = 4;
assert(n % qk == 0);
assert(nr % 4 == 0);
assert(nc % ncols_interleaved == 0);
UNUSED(nb);
UNUSED(ncols_interleaved);
UNUSED(blocklen);
#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
for (int y = 0; y < nr / 4; y++) {
const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
for (int x = 0; x < nc / ncols_interleaved; x++) {
const block_q8_0x4 * b_ptr = (const block_q8_0x4 *) vx + (x * nb);
float32x4_t sumf[4];
for (int m = 0; m < 4; m++) {
sumf[m] = vdupq_n_f32(0);
}
for (int l = 0; l < nb; l++) {
float32x4_t a_d = vcvt_f32_f16(vld1_f16((const float16_t *) a_ptr[l].d));
float32x4_t b_d = vcvt_f32_f16(vld1_f16((const float16_t *) b_ptr[l].d));
int32x4_t sumi_0 = vdupq_n_s32(0);
int32x4_t sumi_1 = vdupq_n_s32(0);
int32x4_t sumi_2 = vdupq_n_s32(0);
int32x4_t sumi_3 = vdupq_n_s32(0);
for (int k_group = 0; k_group < 8; k_group += 4) {
int8x16x4_t a = vld1q_s8_x4(a_ptr[l].qs + 16 * k_group);
int8x16x4_t b = vld1q_s8_x4(b_ptr[l].qs + 16 * k_group);
for (int k = 0; k < 4; k++) {
sumi_0 = vdotq_laneq_s32(sumi_0, b.val[k], a.val[k], 0);
sumi_1 = vdotq_laneq_s32(sumi_1, b.val[k], a.val[k], 1);
sumi_2 = vdotq_laneq_s32(sumi_2, b.val[k], a.val[k], 2);
sumi_3 = vdotq_laneq_s32(sumi_3, b.val[k], a.val[k], 3);
}
}
sumf[0] = vmlaq_f32(sumf[0], vmulq_laneq_f32(b_d, a_d, 0), vcvtq_f32_s32(sumi_0));
sumf[1] = vmlaq_f32(sumf[1], vmulq_laneq_f32(b_d, a_d, 1), vcvtq_f32_s32(sumi_1));
sumf[2] = vmlaq_f32(sumf[2], vmulq_laneq_f32(b_d, a_d, 2), vcvtq_f32_s32(sumi_2));
sumf[3] = vmlaq_f32(sumf[3], vmulq_laneq_f32(b_d, a_d, 3), vcvtq_f32_s32(sumi_3));
}
for (int m = 0; m < 4; m++) {
vst1q_f32(s + (y * 4 + m) * bs + x * 4, sumf[m]);
}
}
}
return;
#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
ggml_gemm_q8_0_4x4_q8_0_generic(n, s, bs, vx, vy, nr, nc);
}
void ggml_gemm_q8_0_4x8_q8_0(int n,
float * GGML_RESTRICT s,
size_t bs,
const void * GGML_RESTRICT vx,
const void * GGML_RESTRICT vy,
int nr,
int nc) {
const int qk = QK8_0;
const int nb = n / qk;
const int ncols_interleaved = 4;
const int blocklen = 8;
assert(n % qk == 0);
assert(nr % 4 == 0);
assert(nc % ncols_interleaved == 0);
UNUSED(nb);
UNUSED(ncols_interleaved);
UNUSED(blocklen);
#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
const block_q8_0x4 * b_ptr_base = (const block_q8_0x4 *) vx;
for (int y = 0; y < nr; y += 4) {
const block_q8_0x4 * a_ptr_base = (const block_q8_0x4 *) vy + (y / 4) * nb;
for (int x = 0; x < nc; x += ncols_interleaved) {
const block_q8_0x4 * b_ptr = b_ptr_base + (x / 4) * nb;
const block_q8_0x4 * a_ptr = a_ptr_base;
float32x4_t acc_f32[4];
for (int i = 0; i < 4; i++) {
acc_f32[i] = vdupq_n_f32(0);
}
for (int b = 0; b < nb; b++) {
int32x4_t acc[4];
for (int i = 0; i < 4; i++) {
acc[i] = vdupq_n_s32(0);
}
// Process 4 chunks of 8 positions each
for (int chunk = 0; chunk < 4; chunk++) {
int8x16_t a01 = vld1q_s8(a_ptr->qs + chunk * 32);
int8x16_t a23 = vld1q_s8(a_ptr->qs + chunk * 32 + 16);
int8x16_t b01 = vld1q_s8(b_ptr->qs + chunk * 32);
int8x16_t b23 = vld1q_s8(b_ptr->qs + chunk * 32 + 16);
acc[0] = vmmlaq_s32(acc[0], a01, b01);
acc[1] = vmmlaq_s32(acc[1], a01, b23);
acc[2] = vmmlaq_s32(acc[2], a23, b01);
acc[3] = vmmlaq_s32(acc[3], a23, b23);
}
// Reorder outputs from 2×2 tiles to row-major
// acc[0] = [r0c0, r0c1, r1c0, r1c1]
// acc[1] = [r0c2, r0c3, r1c2, r1c3]
// acc[2] = [r2c0, r2c1, r3c0, r3c1]
// acc[3] = [r2c2, r2c3, r3c2, r3c3]
int32x4_t row0 = vcombine_s32(vget_low_s32(acc[0]), vget_low_s32(acc[1]));
int32x4_t row1 = vcombine_s32(vget_high_s32(acc[0]), vget_high_s32(acc[1]));
int32x4_t row2 = vcombine_s32(vget_low_s32(acc[2]), vget_low_s32(acc[3]));
int32x4_t row3 = vcombine_s32(vget_high_s32(acc[2]), vget_high_s32(acc[3]));
// Scales
float32x4_t a_d = vcvt_f32_f16(vld1_f16((const __fp16 *) a_ptr->d));
float32x4_t b_d = vcvt_f32_f16(vld1_f16((const __fp16 *) b_ptr->d));
acc_f32[0] = vfmaq_f32(acc_f32[0], vcvtq_f32_s32(row0), vmulq_laneq_f32(b_d, a_d, 0));
acc_f32[1] = vfmaq_f32(acc_f32[1], vcvtq_f32_s32(row1), vmulq_laneq_f32(b_d, a_d, 1));
acc_f32[2] = vfmaq_f32(acc_f32[2], vcvtq_f32_s32(row2), vmulq_laneq_f32(b_d, a_d, 2));
acc_f32[3] = vfmaq_f32(acc_f32[3], vcvtq_f32_s32(row3), vmulq_laneq_f32(b_d, a_d, 3));
a_ptr++;
b_ptr++;
}
for (int row = 0; row < 4; row++) {
vst1q_f32(s + (y + row) * bs + x, acc_f32[row]);
}
}
}
return;
#endif // defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
ggml_gemm_q8_0_4x8_q8_0_generic(n, s, bs, vx, vy, nr, nc);
}

57
ggml/src/ggml-cpu/ggml-cpu.c
View File

@ -3320,13 +3320,33 @@ void ggml_cpu_fp16_to_fp32(const ggml_fp16_t * x, float * y, int64_t n) {
__m128 y_vec = _mm_cvtph_ps(x_vec);
_mm_storeu_ps(y + i, y_vec);
}
#elif defined(__riscv_zvfh)
for (int vl; i < n; i += vl) {
vl = __riscv_vsetvl_e16m1(n - i);
vfloat16m1_t vx = __riscv_vle16_v_f16m1((_Float16 *)&x[i], vl);
vfloat32m2_t vy = __riscv_vfwcvt_f_f_v_f32m2(vx, vl);
__riscv_vse32_v_f32m2(&y[i], vy, vl);
#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfhmin)
// calculate step size
const int epr = __riscv_vsetvlmax_e16m2();
const int step = epr * 2;
const int np = (n & ~(step - 1));
// unroll by 2
for (; i < np; i += step) {
vfloat16m2_t ax0 = __riscv_vle16_v_f16m2((const _Float16*)x + i, epr);
vfloat32m4_t ay0 = __riscv_vfwcvt_f_f_v_f32m4(ax0, epr);
__riscv_vse32_v_f32m4(y + i, ay0, epr);
vfloat16m2_t ax1 = __riscv_vle16_v_f16m2((const _Float16*)x + i + epr, epr);
vfloat32m4_t ay1 = __riscv_vfwcvt_f_f_v_f32m4(ax1, epr);
__riscv_vse32_v_f32m4(y + i + epr, ay1, epr);
}
// leftovers
int vl;
for (i = np; i < n; i += vl) {
vl = __riscv_vsetvl_e16m2(n - i);
vfloat16m2_t ax0 = __riscv_vle16_v_f16m2((const _Float16*)x + i, vl);
vfloat32m4_t ay0 = __riscv_vfwcvt_f_f_v_f32m4(ax0, vl);
__riscv_vse32_v_f32m4(y + i, ay0, vl);
}
#endif
for (; i < n; ++i) {
@ -3371,6 +3391,31 @@ void ggml_cpu_bf16_to_fp32(const ggml_bf16_t * x, float * y, int64_t n) {
(const __m128i *)(x + i))),
16)));
}
#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfbfmin)
// calculate step size
const int epr = __riscv_vsetvlmax_e16m2();
const int step = epr * 2;
const int np = (n & ~(step - 1));
// unroll by 2
for (; i < np; i += step) {
vbfloat16m2_t ax0 = __riscv_vle16_v_bf16m2((const __bf16*)x + i, epr);
vfloat32m4_t ay0 = __riscv_vfwcvtbf16_f_f_v_f32m4(ax0, epr);
__riscv_vse32_v_f32m4(y + i, ay0, epr);
vbfloat16m2_t ax1 = __riscv_vle16_v_bf16m2((const __bf16*)x + i + epr, epr);
vfloat32m4_t ay1 = __riscv_vfwcvtbf16_f_f_v_f32m4(ax1, epr);
__riscv_vse32_v_f32m4(y + i + epr, ay1, epr);
}
// leftovers
int vl;
for (i = np; i < n; i += vl) {
vl = __riscv_vsetvl_e16m2(n - i);
vbfloat16m2_t ax0 = __riscv_vle16_v_bf16m2((const __bf16*)x + i, vl);
vfloat32m4_t ay0 = __riscv_vfwcvtbf16_f_f_v_f32m4(ax0, vl);
__riscv_vse32_v_f32m4(y + i, ay0, vl);
}
#endif
for (; i < n; i++) {
y[i] = GGML_BF16_TO_FP32(x[i]);

286
ggml/src/ggml-cpu/repack.cpp
View File

@ -692,6 +692,100 @@ void ggml_gemv_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
}
}
void ggml_gemv_q8_0_4x4_q8_0_generic(int n,
float * GGML_RESTRICT s,
size_t bs,
const void * GGML_RESTRICT vx,
const void * GGML_RESTRICT vy,
int nr,
int nc) {
const int qk = QK8_0;
const int nb = n / qk;
const int ncols_interleaved = 4;
const int blocklen = 4;
assert(nr == 1);
assert(n % qk == 0);
assert(nc % ncols_interleaved == 0);
UNUSED(bs);
UNUSED(nr);
float sumf[4];
int sumi;
const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
for (int x = 0; x < nc / ncols_interleaved; x++) {
const block_q8_0x4 * b_ptr = (const block_q8_0x4 *) vx + (x * nb);
for (int j = 0; j < ncols_interleaved; j++) {
sumf[j] = 0.0;
}
for (int l = 0; l < nb; l++) {
for (int k = 0; k < (qk / blocklen); k++) {
for (int j = 0; j < ncols_interleaved; j++) {
sumi = 0;
for (int i = 0; i < blocklen; ++i) {
const int v0 = b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i];
sumi += v0 * a_ptr[l].qs[k * blocklen + i];
}
sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
}
}
}
for (int j = 0; j < ncols_interleaved; j++) {
s[x * ncols_interleaved + j] = sumf[j];
}
}
}
void ggml_gemv_q8_0_4x8_q8_0_generic(int n,
float * GGML_RESTRICT s,
size_t bs,
const void * GGML_RESTRICT vx,
const void * GGML_RESTRICT vy,
int nr,
int nc) {
const int qk = QK8_0;
const int nb = n / qk;
const int ncols_interleaved = 4;
const int blocklen = 8;
assert(nr == 1);
assert(n % qk == 0);
assert(nc % ncols_interleaved == 0);
UNUSED(bs);
UNUSED(nr);
float sumf[4];
int sumi;
const block_q8_0 * a_ptr = (const block_q8_0 *) vy;
for (int x = 0; x < nc / ncols_interleaved; x++) {
const block_q8_0x4 * b_ptr = (const block_q8_0x4 *) vx + (x * nb);
for (int j = 0; j < ncols_interleaved; j++) {
sumf[j] = 0.0;
}
for (int l = 0; l < nb; l++) {
for (int k = 0; k < (qk / blocklen); k++) {
for (int j = 0; j < ncols_interleaved; j++) {
sumi = 0;
for (int i = 0; i < blocklen; ++i) {
const int v0 = b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i];
sumi += v0 * a_ptr[l].qs[k * blocklen + i];
}
sumf[j] += sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d);
}
}
}
for (int j = 0; j < ncols_interleaved; j++) {
s[x * ncols_interleaved + j] = sumf[j];
}
}
}
void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
const int qk = QK8_0;
const int nb = n / qk;
@ -1219,8 +1313,129 @@ void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
}
}
void ggml_gemm_q8_0_4x4_q8_0_generic(int n,
float * GGML_RESTRICT s,
size_t bs,
const void * GGML_RESTRICT vx,
const void * GGML_RESTRICT vy,
int nr,
int nc) {
const int qk = QK8_0;
const int nb = n / qk;
const int ncols_interleaved = 4;
const int blocklen = 4;
assert(n % qk == 0);
assert(nr % 4 == 0);
assert(nc % ncols_interleaved == 0);
float sumf[4][4];
int sumi;
for (int y = 0; y < nr / 4; y++) {
const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
for (int x = 0; x < nc / ncols_interleaved; x++) {
const block_q8_0x4 * b_ptr = (const block_q8_0x4 *) vx + (x * nb);
for (int m = 0; m < 4; m++) {
for (int j = 0; j < ncols_interleaved; j++) {
sumf[m][j] = 0.0;
}
}
for (int l = 0; l < nb; l++) {
for (int k = 0; k < (qk / blocklen); k++) {
for (int m = 0; m < 4; m++) {
for (int j = 0; j < ncols_interleaved; j++) {
sumi = 0;
for (int i = 0; i < blocklen; ++i) {
const int v0 = b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i];
sumi += v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i];
}
sumf[m][j] +=
sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
}
}
}
}
for (int m = 0; m < 4; m++) {
for (int j = 0; j < ncols_interleaved; j++) {
s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
}
}
}
}
}
void ggml_gemm_q8_0_4x8_q8_0_generic(int n,
float * GGML_RESTRICT s,
size_t bs,
const void * GGML_RESTRICT vx,
const void * GGML_RESTRICT vy,
int nr,
int nc) {
const int qk = QK8_0;
const int nb = n / qk;
const int ncols_interleaved = 4;
const int blocklen = 8;
assert(n % qk == 0);
assert(nr % 4 == 0);
assert(nc % ncols_interleaved == 0);
float sumf[4][4];
int sumi;
for (int y = 0; y < nr / 4; y++) {
const block_q8_0x4 * a_ptr = (const block_q8_0x4 *) vy + (y * nb);
for (int x = 0; x < nc / ncols_interleaved; x++) {
const block_q8_0x4 * b_ptr = (const block_q8_0x4 *) vx + (x * nb);
for (int m = 0; m < 4; m++) {
for (int j = 0; j < ncols_interleaved; j++) {
sumf[m][j] = 0.0;
}
}
for (int l = 0; l < nb; l++) {
for (int k = 0; k < (qk / blocklen); k++) {
for (int m = 0; m < 4; m++) {
for (int j = 0; j < ncols_interleaved; j++) {
sumi = 0;
for (int i = 0; i < blocklen; ++i) {
const int v0 = b_ptr[l].qs[k * ncols_interleaved * blocklen + j * blocklen + i];
sumi += v0 * a_ptr[l].qs[k * 4 * blocklen + m * blocklen + i];
}
sumf[m][j] +=
sumi * GGML_CPU_FP16_TO_FP32(b_ptr[l].d[j]) * GGML_CPU_FP16_TO_FP32(a_ptr[l].d[m]);
}
}
}
}
for (int m = 0; m < 4; m++) {
for (int j = 0; j < ncols_interleaved; j++) {
s[(y * 4 + m) * bs + x * ncols_interleaved + j] = sumf[m][j];
}
}
}
}
}
} // extern "C"
static block_q8_0x4 make_block_q8_0x4(block_q8_0 * in, unsigned int blck_size_interleave) {
block_q8_0x4 out;
for (int i = 0; i < 4; i++) {
out.d[i] = in[i].d;
}
const int end = QK8_0 * 4 / blck_size_interleave;
for (int i = 0; i < end; ++i) {
int src_id = i % 4;
int src_offset = (i / 4) * blck_size_interleave;
int dst_offset = i * blck_size_interleave;
memcpy(&out.qs[dst_offset], &in[src_id].qs[src_offset], blck_size_interleave);
}
return out;
}
static block_q4_0x4 make_block_q4_0x4(block_q4_0 * in, unsigned int blck_size_interleave) {
block_q4_0x4 out;
@ -1534,6 +1749,38 @@ static int repack_q4_0_to_q4_0_8_bl(struct ggml_tensor * t, int interleave_block
GGML_UNUSED(data_size);
}
static int repack_q8_0_to_q8_0_4_bl(struct ggml_tensor * t,
int interleave_block,
const void * GGML_RESTRICT data,
size_t data_size) {
GGML_ASSERT(t->type == GGML_TYPE_Q8_0);
GGML_ASSERT(interleave_block == 4 || interleave_block == 8);
constexpr int nrows_interleaved = 4;
block_q8_0x4 * dst = (block_q8_0x4 *) t->data;
const block_q8_0 * src = (const block_q8_0 *) data;
block_q8_0 dst_tmp[4];
int nrow = ggml_nrows(t);
int nblocks = t->ne[0] / QK8_0;
GGML_ASSERT(data_size == nrow * nblocks * sizeof(block_q8_0));
if (t->ne[1] % nrows_interleaved != 0 || t->ne[0] % 8 != 0) {
return -1;
}
for (int b = 0; b < nrow; b += nrows_interleaved) {
for (int64_t x = 0; x < nblocks; x++) {
for (int i = 0; i < nrows_interleaved; i++) {
dst_tmp[i] = src[x + i * nblocks];
}
*dst++ = make_block_q8_0x4(dst_tmp, interleave_block);
}
src += nrows_interleaved * nblocks;
}
return 0;
}
static block_iq4_nlx4 make_block_iq4_nlx4(block_iq4_nl * in, unsigned int blck_size_interleave) {
block_iq4_nlx4 out;
@ -1702,6 +1949,14 @@ template <> int repack<block_iq4_nl, 8, 8>(struct ggml_tensor * t, const void *
return repack_iq4_nl_to_iq4_nl_8_bl(t, 8, data, data_size);
}
template <> int repack<block_q8_0, 4, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
return repack_q8_0_to_q8_0_4_bl(t, 4, data, data_size);
}
template <> int repack<block_q8_0, 8, 4>(struct ggml_tensor * t, const void * data, size_t data_size) {
return repack_q8_0_to_q8_0_4_bl(t, 8, data, data_size);
}
// gemv
template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PARAM_TYPE>
void gemv(int, float *, size_t, const void *, const void *, int, int);
@ -1738,6 +1993,14 @@ template <> void gemv<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size
ggml_gemv_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
}
template <> void gemv<block_q8_0, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
ggml_gemv_q8_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
}
template <> void gemv<block_q8_0, 8, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
ggml_gemv_q8_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc);
}
// gemm
template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PARAM_TYPE>
void gemm(int, float *, size_t, const void *, const void *, int, int);
@ -1774,6 +2037,14 @@ template <> void gemm<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0>(int n, float * s, size
ggml_gemm_iq4_nl_8x8_q8_0(n, s, bs, vx, vy, nr, nc);
}
template <> void gemm<block_q8_0, 4, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
ggml_gemm_q8_0_4x4_q8_0(n, s, bs, vx, vy, nr, nc);
}
template <> void gemm<block_q8_0, 8, 4, GGML_TYPE_Q8_0>(int n, float * s, size_t bs, const void * vx, const void * vy, int nr, int nc) {
ggml_gemm_q8_0_4x8_q8_0(n, s, bs, vx, vy, nr, nc);
}
class tensor_traits_base : public ggml::cpu::tensor_traits {
public:
virtual int repack(struct ggml_tensor * t, const void * data, size_t data_size) = 0;
@ -2168,6 +2439,10 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 4, 4, GGML_TYPE_Q8_0> iq4_nl_4x4_q8_0;
static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0> iq4_nl_8x8_q8_0;
// instance for Q8_0
static const ggml::cpu::repack::tensor_traits<block_q8_0, 4, 4, GGML_TYPE_Q8_0> q8_0_4x4_q8_0;
static const ggml::cpu::repack::tensor_traits<block_q8_0, 8, 4, GGML_TYPE_Q8_0> q8_0_4x8_q8_0;
if (cur->type == GGML_TYPE_Q4_0) {
if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)
|| (ggml_cpu_has_riscv_v() && (ggml_cpu_get_rvv_vlen() >= QK4_0))) {
@ -2218,6 +2493,17 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
return &iq4_nl_4x4_q8_0;
}
}
} else if (cur->type == GGML_TYPE_Q8_0) {
if (ggml_cpu_has_neon() && ggml_cpu_has_matmul_int8()) {
if (cur->ne[1] % 4 == 0) {
return &q8_0_4x8_q8_0;
}
}
if (ggml_cpu_has_neon() && ggml_cpu_has_dotprod()) {
if (cur->ne[1] % 4 == 0) {
return &q8_0_4x4_q8_0;
}
}
}
return nullptr;

8
ggml/src/ggml-cpu/repack.h
View File

@ -98,6 +98,10 @@ void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const vo
void ggml_gemm_q2_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_iq4_nl_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q8_0_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q8_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
// Native implementations
void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
@ -120,6 +124,10 @@ void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
void ggml_gemm_q2_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_iq4_nl_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemv_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q8_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
void ggml_gemm_q8_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
#if defined(__cplusplus)
} // extern "C"

42
ggml/src/ggml-cpu/vec.cpp
View File

@ -195,8 +195,48 @@ void ggml_vec_dot_bf16(int n, float * GGML_RESTRICT s, size_t bs, ggml_bf16_t *
sumf += (ggml_float)_mm_cvtss_f32(g);
#undef LOAD
#endif
#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfbfwma)
size_t vl = __riscv_vsetvlmax_e32m4();
// initialize accumulators to all zeroes
vfloat32m4_t vsum0 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
vfloat32m4_t vsum1 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
// calculate step size
const size_t epr = __riscv_vsetvlmax_e16m2();
const size_t step = epr * 2;
const int np = (n & ~(step - 1));
// unroll by 2
for (; i < np; i += step) {
vbfloat16m2_t ax0 = __riscv_vle16_v_bf16m2((const __bf16 *)&x[i], epr);
vbfloat16m2_t ay0 = __riscv_vle16_v_bf16m2((const __bf16 *)&y[i], epr);
vsum0 = __riscv_vfwmaccbf16_vv_f32m4(vsum0, ax0, ay0, epr);
__asm__ __volatile__ ("" ::: "memory");
vbfloat16m2_t ax1 = __riscv_vle16_v_bf16m2((const __bf16 *)&x[i + epr], epr);
vbfloat16m2_t ay1 = __riscv_vle16_v_bf16m2((const __bf16 *)&y[i + epr], epr);
vsum1 = __riscv_vfwmaccbf16_vv_f32m4(vsum1, ax1, ay1, epr);
__asm__ __volatile__ ("" ::: "memory");
}
// accumulate in 1 register
vsum0 = __riscv_vfadd_vv_f32m4(vsum0, vsum1, vl);
// leftovers
for (i = np; i < n; i += vl) {
vl = __riscv_vsetvl_e16m2(n - i);
vbfloat16m2_t ax0 = __riscv_vle16_v_bf16m2((const __bf16 *)&x[i], vl);
vbfloat16m2_t ay0 = __riscv_vle16_v_bf16m2((const __bf16 *)&y[i], vl);
vsum0 = __riscv_vfwmaccbf16_vv_f32m4(vsum0, ax0, ay0, vl);
}
// reduce
vl = __riscv_vsetvlmax_e32m4();
vfloat32m1_t redsum = __riscv_vfredusum_vs_f32m4_f32m1(vsum0, __riscv_vfmv_v_f_f32m1(0.0f, 1), vl);
sumf += __riscv_vfmv_f_s_f32m1_f32(redsum);
#endif
for (; i < n; ++i) {
sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) *
GGML_BF16_TO_FP32(y[i]));

147
ggml/src/ggml-cpu/vec.h
View File

@ -224,13 +224,71 @@ inline static void ggml_vec_dot_f16_unroll(const int n, const int xs, float * GG
}
GGML_F16x_VEC_REDUCE(sumf[0], sum_00, sum_01, sum_02, sum_03);
GGML_F16x_VEC_REDUCE(sumf[1], sum_10, sum_11, sum_12, sum_13);
#elif defined(__riscv_v_intrinsic)
// todo: RVV impl
for (int i = 0; i < n; ++i) {
for (int j = 0; j < GGML_VEC_DOT_UNROLL; ++j) {
sumf[j] += (ggml_float)(GGML_CPU_FP16_TO_FP32(x[j][i])*GGML_CPU_FP16_TO_FP32(y[i]));
}
}
#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfh)
size_t vl = __riscv_vsetvlmax_e32m4();
// initialize accumulators to all zeroes
vfloat32m4_t vsum0_0 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
vfloat32m4_t vsum0_1 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
vfloat32m4_t vsum1_0 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
vfloat32m4_t vsum1_1 = __riscv_vfmv_v_f_f32m4(0.0f, vl);
// calculate step size
const size_t epr = __riscv_vsetvlmax_e16m2();
const size_t step = epr * 2;
const int np = (n & ~(step - 1));
// unroll by 2 along the row dimension
for (int i = 0; i < np; i += step) {
vfloat16m2_t ay0 = __riscv_vle16_v_f16m2((const _Float16 *)(y + i), epr);
vfloat16m2_t ax0_0 = __riscv_vle16_v_f16m2((const _Float16 *)(x[0] + i), epr);
vfloat16m2_t ax1_0 = __riscv_vle16_v_f16m2((const _Float16 *)(x[1] + i), epr);
vsum0_0 = __riscv_vfwmacc_vv_f32m4(vsum0_0, ax0_0, ay0, epr);
vsum1_0 = __riscv_vfwmacc_vv_f32m4(vsum1_0, ax1_0, ay0, epr);
vfloat16m2_t ay1 = __riscv_vle16_v_f16m2((const _Float16 *)(y + i + epr), epr);
vfloat16m2_t ax0_1 = __riscv_vle16_v_f16m2((const _Float16 *)(x[0] + i + epr), epr);
vfloat16m2_t ax1_1 = __riscv_vle16_v_f16m2((const _Float16 *)(x[1] + i + epr), epr);
vsum0_1 = __riscv_vfwmacc_vv_f32m4(vsum0_1, ax0_1, ay1, epr);
vsum1_1 = __riscv_vfwmacc_vv_f32m4(vsum1_1, ax1_1, ay1, epr);
}
vfloat32m4_t vsum0 = __riscv_vfadd_vv_f32m4(vsum0_0, vsum0_1, vl);
vfloat32m4_t vsum1 = __riscv_vfadd_vv_f32m4(vsum1_0, vsum1_1, vl);
// leftovers
for (int i = np; i < n; i += vl) {
vl = __riscv_vsetvl_e16m2(n - i);
vfloat16m2_t ay = __riscv_vle16_v_f16m2((const _Float16 *)(y + i), vl);
vfloat16m2_t ax0 = __riscv_vle16_v_f16m2((const _Float16 *)(x[0] + i), vl);
vfloat16m2_t ax1 = __riscv_vle16_v_f16m2((const _Float16 *)(x[1] + i), vl);
vsum0 = __riscv_vfwmacc_vv_f32m4(vsum0, ax0, ay, vl);
vsum1 = __riscv_vfwmacc_vv_f32m4(vsum1, ax1, ay, vl);
}
// reduce
vl = __riscv_vsetvlmax_e32m2();
vfloat32m2_t acc0_0 = __riscv_vfadd_vv_f32m2(__riscv_vget_v_f32m4_f32m2(vsum0, 0),
__riscv_vget_v_f32m4_f32m2(vsum0, 1), vl);
vl = __riscv_vsetvlmax_e32m1();
vfloat32m1_t acc0_1 = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m2_f32m1(acc0_0, 0),
__riscv_vget_v_f32m2_f32m1(acc0_0, 1), vl);
vfloat32m1_t redsum0 = __riscv_vfredusum_vs_f32m1_f32m1(
acc0_1, __riscv_vfmv_v_f_f32m1(0.0f, 1), vl);
vl = __riscv_vsetvlmax_e32m2();
vfloat32m2_t acc1_0 = __riscv_vfadd_vv_f32m2(__riscv_vget_v_f32m4_f32m2(vsum1, 0),
__riscv_vget_v_f32m4_f32m2(vsum1, 1), vl);
vl = __riscv_vsetvlmax_e32m1();
vfloat32m1_t acc1_1 = __riscv_vfadd_vv_f32m1(__riscv_vget_v_f32m2_f32m1(acc1_0, 0),
__riscv_vget_v_f32m2_f32m1(acc1_0, 1), vl);
vfloat32m1_t redsum1 = __riscv_vfredusum_vs_f32m1_f32m1(
acc1_1, __riscv_vfmv_v_f_f32m1(0.0f, 1), vl);
sumf[0] = __riscv_vfmv_f_s_f32m1_f32(redsum0);
sumf[1] = __riscv_vfmv_f_s_f32m1_f32(redsum1);
#else
const int np = (n & ~(GGML_F16_STEP - 1));
@ -475,15 +533,39 @@ inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * GGML_RESTRICT y,
}
np = n;
#elif defined(__riscv_zvfh) // implies __riscv_v_intrinsic
const int np = n;
_Float16 hv = (_Float16)v;
for (int i = 0, avl; i < n; i += avl) {
avl = __riscv_vsetvl_e16m8(n - i);
vfloat16m8_t ax = __riscv_vle16_v_f16m8((const _Float16 *)&x[i], avl);
vfloat16m8_t ay = __riscv_vle16_v_f16m8((_Float16 *)&y[i], avl);
vfloat16m8_t ny = __riscv_vfmadd_vf_f16m8(ax, hv, ay, avl);
__riscv_vse16_v_f16m8((_Float16 *)&y[i], ny, avl);
const ggml_fp16_t s = GGML_CPU_FP32_TO_FP16(v);
const _Float16 scale = *(const _Float16*)(&s);
// calculate step size
const int epr = __riscv_vsetvlmax_e16m4();
const int step = epr * 2;
int np = (n & ~(step - 1));
// unroll by 2
for (int i = 0; i < np; i += step) {
vfloat16m4_t ax0 = __riscv_vle16_v_f16m4((const _Float16*)x + i, epr);
vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, epr);
ay0 = __riscv_vfmacc_vf_f16m4(ay0, scale, ax0, epr);
__riscv_vse16_v_f16m4((_Float16*)y + i, ay0, epr);
__asm__ __volatile__ ("" ::: "memory");
vfloat16m4_t ax1 = __riscv_vle16_v_f16m4((const _Float16*)x + i + epr, epr);
vfloat16m4_t ay1 = __riscv_vle16_v_f16m4((const _Float16*)y + i + epr, epr);
ay1 = __riscv_vfmacc_vf_f16m4(ay1, scale, ax1, epr);
__riscv_vse16_v_f16m4((_Float16*)y + i + epr, ay1, epr);
__asm__ __volatile__ ("" ::: "memory");
}
// leftovers
int vl;
for (int i = np; i < n; i += vl) {
vl = __riscv_vsetvl_e16m4(n - i);
vfloat16m4_t ax0 = __riscv_vle16_v_f16m4((const _Float16*)x + i, vl);
vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, vl);
ay0 = __riscv_vfmacc_vf_f16m4(ay0, scale, ax0, vl);
__riscv_vse16_v_f16m4((_Float16*)y + i, ay0, vl);
}
np = n;
#elif defined(GGML_SIMD)
const int np = (n & ~(GGML_F16_STEP - 1));
@ -724,13 +806,34 @@ inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float
svst1_f16(pg, (__fp16 *)(y + np), out);
}
#elif defined(__riscv_v_intrinsic) && defined(__riscv_zvfh)
for (int i = 0, vl; i < n; i += vl) {
vl = __riscv_vsetvl_e16m2(n - i);
vfloat16m2_t vy = __riscv_vle16_v_f16m2((_Float16 *)&y[i], vl);
vfloat32m4_t vy32 = __riscv_vfwcvt_f_f_v_f32m4(vy, vl);
vy32 = __riscv_vfmul_vf_f32m4(vy32, v, vl);
vy = __riscv_vfncvt_f_f_w_f16m2(vy32, vl);
__riscv_vse16_v_f16m2((_Float16 *)&y[i], vy, vl);
const ggml_fp16_t s = GGML_CPU_FP32_TO_FP16(v);
const _Float16 scale = *(const _Float16*)(&s);
// calculate step size
const int epr = __riscv_vsetvlmax_e16m4();
const int step = epr * 2;
const int np = (n & ~(step - 1));
// unroll by 2
for (int i = 0; i < np; i += step) {
vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, epr);
ay0 = __riscv_vfmul_vf_f16m4(ay0, scale, epr);
__riscv_vse16_v_f16m4((_Float16*)y + i, ay0, epr);
__asm__ __volatile__ ("" ::: "memory");
vfloat16m4_t ay1 = __riscv_vle16_v_f16m4((const _Float16*)y + i + epr, epr);
ay1 = __riscv_vfmul_vf_f16m4(ay1, scale, epr);
__riscv_vse16_v_f16m4((_Float16*)y + i + epr, ay1, epr);
__asm__ __volatile__ ("" ::: "memory");
}
// leftovers
int vl;
for (int i = np; i < n; i += vl) {
vl = __riscv_vsetvl_e16m4(n - i);
vfloat16m4_t ay0 = __riscv_vle16_v_f16m4((const _Float16*)y + i, vl);
ay0 = __riscv_vfmul_vf_f16m4(ay0, scale, vl);
__riscv_vse16_v_f16m4((_Float16*)y + i, ay0, vl);
}
#elif defined(GGML_SIMD)
const int np = (n & ~(GGML_F16_STEP - 1));

4
ggml/src/ggml-cuda/argmax.cu
View File

@ -21,7 +21,7 @@ static __global__ void argmax_f32(const float * __restrict__ x, int32_t * __rest
}
#pragma unroll
for (int offset = 16; offset > 0; offset >>= 1) {
for (int offset = WARP_SIZE/2; offset > 0; offset >>= 1) {
const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
if (val > maxval) {
@ -50,7 +50,7 @@ static __global__ void argmax_f32(const float * __restrict__ x, int32_t * __rest
argmax = shared_argmax[lane_id];
}
#pragma unroll
for (int offset = 16; offset > 0; offset >>= 1) {
for (int offset = WARP_SIZE/2; offset > 0; offset >>= 1) {
const float val = __shfl_xor_sync(0xFFFFFFFF, maxval, offset, WARP_SIZE);
const int col = __shfl_xor_sync(0xFFFFFFFF, argmax, offset, WARP_SIZE);
if (val > maxval) {

16
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@ -3076,8 +3076,11 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 3, node_idx + 9 })) {
ggml_tensor * softmax = cgraph->nodes[node_idx];
ggml_tensor * weights = cgraph->nodes[node_idx + 9];
ggml_tensor * get_rows = cgraph->nodes[node_idx + 4];
ggml_tensor * argsort = cgraph->nodes[node_idx + 2];
int n_expert = cgraph->nodes[node_idx]->src[0]->ne[0];
if (ggml_cuda_should_use_topk_moe(softmax, weights)) {
if (ggml_cuda_should_use_topk_moe(softmax, weights, get_rows, argsort, nullptr, n_expert)) {
return true;
}
}
@ -3085,7 +3088,11 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
if (is_equal(topk_moe_ops, ops) && ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 3, node_idx + 4 })) {
ggml_tensor * softmax = cgraph->nodes[node_idx];
ggml_tensor * weights = cgraph->nodes[node_idx + 4];
if (ggml_cuda_should_use_topk_moe(softmax, weights)) {
ggml_tensor * get_rows = cgraph->nodes[node_idx + 4];
ggml_tensor * argsort = cgraph->nodes[node_idx + 2];
int n_expert = cgraph->nodes[node_idx]->src[0]->ne[0];
if (ggml_cuda_should_use_topk_moe(softmax, weights, get_rows, argsort, nullptr, n_expert)) {
return true;
}
}
@ -3094,8 +3101,11 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 1, node_idx + 5 })) {
ggml_tensor * softmax = cgraph->nodes[node_idx + 4];
ggml_tensor * weights = cgraph->nodes[node_idx + 5];
ggml_tensor * get_rows = cgraph->nodes[node_idx + 2];
ggml_tensor * argsort = cgraph->nodes[node_idx + 0];
int n_expert = cgraph->nodes[node_idx]->src[0]->ne[0];
if (ggml_cuda_should_use_topk_moe(softmax, weights)) {
if (ggml_cuda_should_use_topk_moe(softmax, weights, get_rows, argsort, nullptr, n_expert)) {
return true;
}
}

3
ggml/src/ggml-cuda/mean.cu
View File

@ -63,6 +63,9 @@ void ggml_cuda_op_mean(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const int id = ggml_cuda_get_device();
const int nsm = ggml_cuda_info().devices[id].nsm;
// Heuristic for block size selection to optimize occupancy.
// See discussion in: https://github.com/ggml-org/llama.cpp/pull/15132
if ((nrows / nsm) < 2) {
const dim3 block_dims(512, 1, 1);
reduce_rows_f32</*norm=*/true><<<block_nums, block_dims, 0, stream>>>(src0_d, dst_d, ncols);

279
ggml/src/ggml-cuda/mma.cuh
View File

@ -76,15 +76,29 @@ namespace ggml_cuda_mma {
// For the A/C matrices this means I major == row major, J major == column major.
// For the B matrix this means I major == column major, J major == row major.
// MIRRORED == Each data value is held exactly once per thread subgroup.
DATA_LAYOUT_I_MAJOR = 0, // Always used for Turing, Ampere, Ada Lovelace, consumer Blackwell.
DATA_LAYOUT_I_MAJOR_MIRRORED = 10,
DATA_LAYOUT_J_MAJOR_MIRRORED = 20,
DATA_LAYOUT_I_MAJOR = 0, // Always used for Turing, Ampere, Ada Lovelace, consumer Blackwell, matrix A&B for RDNA4 and CDNA.
DATA_LAYOUT_J_MAJOR = 10, // Matrix C for CDNA and RDNA4, int and float matrix C for RDNA3.
DATA_LAYOUT_I_MAJOR_MIRRORED = 20, // Volta, matrix A&B for RDNA3.
DATA_LAYOUT_J_MAJOR_MIRRORED = 30,
};
// Implemented mma combinations are:
// - (I_MAJOR, I_MAJOR) -> I_MAJOR
// - (I_MAJOR, I_MAJOR_MIRRORED) -> I_MAJOR
// - (I_MAJOR, J_MAJOR_MIRRORED) -> I_MAJOR
static constexpr bool is_i_major(const data_layout dl) {
return dl == DATA_LAYOUT_I_MAJOR ||
dl == DATA_LAYOUT_I_MAJOR_MIRRORED;
}
static constexpr __device__ data_layout get_input_data_layout() {
#if defined(RDNA3) || __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
return DATA_LAYOUT_I_MAJOR_MIRRORED;
#else
return DATA_LAYOUT_I_MAJOR;
#endif // defined(RDNA3) || __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
}
template <int I_, int J_, typename T, data_layout ds_=DATA_LAYOUT_I_MAJOR>
struct tile {};
@ -115,9 +129,9 @@ namespace ggml_cuda_mma {
} else if constexpr (I == 32 && J == 4) {
return threadIdx.x % 32;
} else if constexpr (I == 16 && J == 16) {
return 4 * (threadIdx.x / 16) + l;
return threadIdx.x % 16;
} else if constexpr (I == 32 && J == 32) {
return 4 * (threadIdx.x / 32) + 8 * (l / 4) + (l % 4);
return threadIdx.x % 32;
} else {
NO_DEVICE_CODE;
return -1;
@ -132,9 +146,9 @@ namespace ggml_cuda_mma {
} else if constexpr (I == 32 && J == 4) {
return 2 * (threadIdx.x / 32) + l;
} else if constexpr (I == 16 && J == 16) {
return threadIdx.x % 16;
return 4 * (threadIdx.x / 16) + l;
} else if constexpr (I == 32 && J == 32) {
return threadIdx.x % 32;
return 4 * (threadIdx.x / 32) + 8 * (l / 4) + (l % 4);
} else {
NO_DEVICE_CODE;
return -1;
@ -171,28 +185,19 @@ namespace ggml_cuda_mma {
}
}
#elif defined(AMD_WMMA_AVAILABLE)
#if defined(RDNA4)
static constexpr int ne = I * J / 32;
#elif defined(RDNA3)
static constexpr int ne = (I == 16 && J == 16) ? I * J / 32 : I * J / 16;
#endif // defined(RDNA4)
T x[ne] = {0};
static constexpr __device__ bool supported() {
if (I == 16 && J == 16) return true;
if (I == 16 && J == 8) return true;
if (I == 16 && J == 4) return true;
return false;
}
static __device__ __forceinline__ int get_i(const int l) {
if constexpr (I == 16 && J == 16) {
#if defined(RDNA4)
return 8 * (threadIdx.x / 16) + l;
#elif defined(RDNA3)
return 2 * l + (threadIdx.x / 16);
#else
NO_DEVICE_CODE;
return -1;
#endif // defined(RDNA4)
if constexpr (supported()) {
return threadIdx.x % 16;
} else {
NO_DEVICE_CODE;
return -1;
@ -201,7 +206,17 @@ namespace ggml_cuda_mma {
static __device__ __forceinline__ int get_j(const int l) {
if constexpr (I == 16 && J == 16) {
return threadIdx.x % 16;
// matrix C
#if defined(RDNA3)
return 2 * l + (threadIdx.x / 16);
#else
return ne * (threadIdx.x / 16) + l;
#endif // defined(RDNA3)
} else if constexpr (I == 16 && J == 8) {
// mmq input for RDNA4
return ne * (threadIdx.x / 16) + l;
} else if constexpr (I == 16 && J == 4) {
return ne * (threadIdx.x / 16) + l;
} else {
NO_DEVICE_CODE;
return -1;
@ -293,12 +308,7 @@ namespace ggml_cuda_mma {
}
}
#elif defined(AMD_WMMA_AVAILABLE)
#if defined(RDNA3)
// RDNA3 has duplicated data as input.
static constexpr int ne = I * J / 32 * 2;
#else
static constexpr int ne = I * J / 32;
#endif // defined(RDNA3)
half2 x[ne] = {{0.0f, 0.0f}};
static constexpr __device__ bool supported() {
@ -317,14 +327,7 @@ namespace ggml_cuda_mma {
static __device__ __forceinline__ int get_j(const int l) {
if constexpr (I == 16 && J == 8) {
#if defined(RDNA4)
return 4 * (threadIdx.x / 16) + l;
#elif defined(RDNA3)
return l;
#else
NO_DEVICE_CODE;
return -1;
#endif // defined(RDNA4)
} else {
NO_DEVICE_CODE;
return -1;
@ -382,42 +385,19 @@ namespace ggml_cuda_mma {
static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR;
#if defined(AMD_WMMA_AVAILABLE)
#if defined(RDNA3)
// RDNA3 has duplicated data as input.
static constexpr int ne = I * J / 32 * 2;
#else
static constexpr int ne = I * J / 32;
#endif // defined(RDNA3)
nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
static constexpr __device__ bool supported() {
if (I == 16 && J == 8) return true;
return false;
return tile<I_, J_, half2, DATA_LAYOUT_I_MAJOR>::supported();
}
static __device__ __forceinline__ int get_i(const int l) {
if constexpr (I == 16 && J == 8) {
return threadIdx.x % 16;
} else {
NO_DEVICE_CODE;
return -1;
}
return tile<I_, J_, half2, DATA_LAYOUT_I_MAJOR>::get_i(l);
}
static __device__ __forceinline__ int get_j(const int l) {
if constexpr (I == 16 && J == 8) {
#if defined(RDNA4)
return 4 * (threadIdx.x / 16) + l;
#elif defined(RDNA3)
return l;
#else
NO_DEVICE_CODE;
return -1;
#endif // defined(RDNA4)
} else {
NO_DEVICE_CODE;
return -1;
}
return tile<I_, J_, half2, DATA_LAYOUT_I_MAJOR>::get_j(l);
}
#else
static constexpr int ne = I * J / WARP_SIZE;
@ -458,11 +438,87 @@ namespace ggml_cuda_mma {
#endif // defined(AMD_WMMA_AVAILABLE)
};
template <int I_, int J_, typename T>
struct tile<I_, J_, T, DATA_LAYOUT_J_MAJOR> {
static constexpr int I = I_;
static constexpr int J = J_;
static constexpr data_layout dl = DATA_LAYOUT_J_MAJOR;
static constexpr int ne = tile<I_, J_, T, DATA_LAYOUT_I_MAJOR>::ne;
T x[ne] = {0};
static constexpr __device__ bool supported() {
return tile<I_, J_, T, DATA_LAYOUT_I_MAJOR>::supported();
}
static __device__ __forceinline__ int get_i(const int l) {
return tile<I_, J_, T, DATA_LAYOUT_I_MAJOR>::get_j(l);
}
static __device__ __forceinline__ int get_j(const int l) {
return tile<I_, J_, T, DATA_LAYOUT_I_MAJOR>::get_i(l);
}
};
template <int I_, int J_, typename T>
struct tile<I_, J_, T, DATA_LAYOUT_I_MAJOR_MIRRORED> {
static constexpr int I = I_;
static constexpr int J = J_;
static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR_MIRRORED;
// RDNA3
static constexpr int ne = I * J / 32 * 2;
T x[ne] = {0};
static constexpr __device__ bool supported() {
if (I == 16 && J == 16) return true;
if (I == 16 && J == 8) return true;
if (I == 16 && J == 4) return true;
return false;
}
static __device__ __forceinline__ int get_i(const int /*l*/) {
if constexpr (supported()) {
return threadIdx.x % 16;
} else {
NO_DEVICE_CODE;
return -1;
}
}
static __device__ __forceinline__ int get_j(const int l) {
if constexpr (supported()) {
return l;
} else {
NO_DEVICE_CODE;
return -1;
}
}
};
template <int I_, int J_>
struct tile<I_, J_, half2, DATA_LAYOUT_I_MAJOR_MIRRORED> {
static constexpr int I = I_;
static constexpr int J = J_;
static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR_MIRRORED;
#if defined(RDNA3)
static constexpr int ne = tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::ne;
half2 x[ne] = {{0.0f, 0.0f}};
static constexpr __device__ bool supported() {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::supported();
}
static __device__ __forceinline__ int get_i(const int l) {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::get_i(l);
}
static __device__ __forceinline__ int get_j(const int l) {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::get_j(l);
}
#else // Volta
static constexpr int ne = I * J / (WARP_SIZE/4);
half2 x[ne] = {{0.0f, 0.0f}};
@ -489,6 +545,29 @@ namespace ggml_cuda_mma {
return -1;
}
}
#endif // defined(RDNA3)
};
template <int I_, int J_>
struct tile<I_, J_, nv_bfloat162, DATA_LAYOUT_I_MAJOR_MIRRORED> {
static constexpr int I = I_;
static constexpr int J = J_;
static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR_MIRRORED;
static constexpr int ne = tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::ne;
nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
static constexpr __device__ bool supported() {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::supported();
}
static __device__ __forceinline__ int get_i(const int l) {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::get_i(l);
}
static __device__ __forceinline__ int get_j(const int l) {
return tile<I_, J_, float, DATA_LAYOUT_I_MAJOR_MIRRORED>::get_j(l);
}
};
template <int I_, int J_>
@ -569,55 +648,28 @@ namespace ggml_cuda_mma {
t.x[l] = xs0[t.get_i(l)*stride + t.get_j(l)];
}
} else {
int64_t * xi = (int64_t *) t.x;
const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 2 * (threadIdx.x / t.I));
xi[0] = xs[0];
ggml_cuda_memcpy_1<sizeof(t.x)>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
}
#elif defined(AMD_WMMA_AVAILABLE)
if constexpr (std::is_same_v<T, half2> || std::is_same_v<T, nv_bfloat162>) {
#if defined(RDNA4)
ggml_cuda_memcpy_1<sizeof(t.x)>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
#elif defined(RDNA3)
ggml_cuda_memcpy_1<sizeof(t.x)/2>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
ggml_cuda_memcpy_1<sizeof(t.x)/2>(t.x + t.ne/2, xs0 + t.get_i(0) * stride + t.get_j(t.ne/2));
#else
NO_DEVICE_CODE;
#endif // defined(RDNA4)
} else if constexpr (std::is_same_v<T, int>) {
if constexpr (I == 16 && J == 4) {
int64_t * xi = (int64_t *) t.x;
#if defined(RDNA4)
const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 2 * (threadIdx.x / t.I));
xi[0] = xs[0];
#elif defined(RDNA3)
static_assert(tile<I,J,T>::ne >= 4, "fragment too small");
const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride);
xi[0] = xs[0];
xi[1] = xs[1];
#endif // defined(RDNA4)
} else if constexpr (I == 16 && J == 8) {
int64_t * xi = (int64_t *) t.x;
#if defined(RDNA4)
const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 4 * (threadIdx.x / t.I));
xi[0] = xs[0];
const int64_t * xs1 = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 4 * (threadIdx.x / t.I) + 2);
xi[1] = xs1[0];
#elif defined(RDNA3)
static_assert(tile<I,J,T>::ne >= 8, "fragment too small");
const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride);
// contiguous four 64-bit chunks per lane for the wider RDNA3 fragment
xi[0] = xs[0];
xi[1] = xs[1];
const int64_t * xs1 = xs + 2;
xi[2] = xs1[0];
xi[3] = xs1[1];
#endif // defined(RDNA4)
// All wmma layout has contiguous data when i-major.
if constexpr (is_i_major(dl)) {
// the data must be aligned to 16 bytes when bigger than ggml_cuda_get_max_cpy_bytes()
constexpr int aligned_copy_bytes = ggml_cuda_get_max_cpy_bytes();
if constexpr (sizeof(t.x) > aligned_copy_bytes) {
static_assert(sizeof(t.x) % aligned_copy_bytes == 0, "bad type size");
constexpr int aligned_copy_count = sizeof(t.x)/aligned_copy_bytes;
#pragma unroll
for (int i = 0; i < aligned_copy_count; ++i) {
ggml_cuda_memcpy_1<aligned_copy_bytes>(t.x + t.ne/aligned_copy_count*i, xs0 + t.get_i(0) * stride + t.get_j(t.ne/aligned_copy_count*i));
}
} else {
NO_DEVICE_CODE;
ggml_cuda_memcpy_1<sizeof(t.x)>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
}
} else {
NO_DEVICE_CODE;
#pragma unroll
for (int l = 0; l < t.ne; ++l) {
t.x[l] = xs0[t.get_i(l)*stride + t.get_j(l)];
}
}
#else
#pragma unroll
@ -660,9 +712,9 @@ namespace ggml_cuda_mma {
#endif // TURING_MMA_AVAILABLE
}
template <typename T>
template <typename T, data_layout dl>
static __device__ __forceinline__ void load_ldmatrix(
tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
tile<16, 8, T, dl> & t, const T * __restrict__ xs0, const int stride) {
#if defined(TURING_MMA_AVAILABLE)
int * xi = (int * ) t.x;
const int * xs = (const int *) xs0 + (threadIdx.x % t.I) * stride + (threadIdx.x / t.I) * (t.J / 2);
@ -832,8 +884,9 @@ namespace ggml_cuda_mma {
#endif // TURING_MMA_AVAILABLE
}
template <data_layout dl_ab, data_layout dl_d>
static __device__ __forceinline__ void mma(
tile<16, 8, float> & D, const tile<16, 8, float> & A, const tile<8, 8, float> & B) {
tile<16, 8, float, dl_d> & D, const tile<16, 8, float, dl_ab> & A, const tile<8, 8, float, dl_ab> & B) {
#ifdef AMPERE_MMA_AVAILABLE
const int * Axi = (const int *) A.x;
const int * Bxi = (const int *) B.x;
@ -887,8 +940,9 @@ namespace ggml_cuda_mma {
#endif // AMPERE_MMA_AVAILABLE
}
template <data_layout dl_ab, data_layout dl_d>
static __device__ __forceinline__ void mma(
tile<16, 16, float> & D, const tile<16, 8, half2> & A, const tile<16, 8, half2> & B) {
tile<16, 16, float, dl_d> & D, const tile<16, 8, half2, dl_ab> & A, const tile<16, 8, half2, dl_ab> & B) {
#ifdef TURING_MMA_AVAILABLE
const int * Axi = (const int *) A.x;
const int * Bxi = (const int *) B.x;
@ -940,8 +994,9 @@ namespace ggml_cuda_mma {
#endif // TURING_MMA_AVAILABLE
}
template <data_layout dl_ab, data_layout dl_d>
static __device__ __forceinline__ void mma(
tile<16, 16, float> & D, const tile<16, 8, nv_bfloat162> & A, const tile<16, 8, nv_bfloat162> & B) {
tile<16, 16, float, dl_d> & D, const tile<16, 8, nv_bfloat162, dl_ab> & A, const tile<16, 8, nv_bfloat162, dl_ab> & B) {
#if defined(AMD_WMMA_AVAILABLE)
#if defined(RDNA4)
using bf16x8_t = __attribute__((ext_vector_type(8))) __bf16;
@ -967,8 +1022,9 @@ namespace ggml_cuda_mma {
#endif // AMPERE_MMA_AVAILABLE
}
template <data_layout dl_d, data_layout dl_ab>
static __device__ __forceinline__ void mma(
tile<16, 16, int> & D, const tile<16, 8, int> & A, const tile<16, 8, int> & B) {
tile<16, 16, int, dl_d> & D, const tile<16, 8, int, dl_ab> & A, const tile<16, 8, int, dl_ab> & B) {
#if defined(AMD_MFMA_AVAILABLE)
using int32x4_t = __attribute__((__vector_size__(4 * sizeof(int)))) int;
int32x4_t * acc = (int32x4_t *) D.x;
@ -1122,8 +1178,9 @@ namespace ggml_cuda_mma {
#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_VOLTA
}
static __device__ __forceinline__ void mma(
tile<16, 16, int> & D, const tile<16, 4, int> & A, const tile<16, 4, int> & B) {
template <data_layout dl_d, data_layout dl_ab>
static __device__ __forceinline__ void mma(
tile<16, 16, int, dl_d> & D, const tile<16, 4, int, dl_ab> & A, const tile<16, 4, int, dl_ab> & B) {
#if defined(AMD_WMMA_AVAILABLE)
using int32x8_t = __attribute__((__vector_size__(8 * sizeof(int)))) int;
int32x8_t * acc = (int32x8_t *) D.x;

24
ggml/src/ggml-cuda/mmf.cuh
View File

@ -32,11 +32,13 @@ static __global__ void mul_mat_f(
#if (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE)
#if defined(AMD_WMMA_AVAILABLE)
// Special case for tf32, just dummy mma layout as wmma doesn't support it.
constexpr int tile_B_I = std::is_same_v<T, float> ? 8 : 16;
constexpr int tile_C_J = std::is_same_v<T, float> ? 8 : 16;
typedef tile<16, 8, T> tile_A;
typedef tile<tile_B_I, 8, T> tile_B;
typedef tile<16, tile_C_J, float> tile_C;
constexpr bool is_tf32 = std::is_same_v<T, float>;
constexpr int tile_B_I = is_tf32 ? 8 : 16;
constexpr int tile_C_J = is_tf32 ? 8 : 16;
constexpr data_layout ab_layout = is_tf32 ? DATA_LAYOUT_I_MAJOR : get_input_data_layout();
typedef tile<16, 8, T, ab_layout> tile_A;
typedef tile<tile_B_I, 8, T, ab_layout> tile_B;
typedef tile<16, tile_C_J, float, DATA_LAYOUT_J_MAJOR> tile_C;
#else
#ifdef VOLTA_MMA_AVAILABLE
if constexpr (!std::is_same_v<T, half2>) {NO_DEVICE_CODE;} else {
@ -272,11 +274,13 @@ static __global__ void mul_mat_f_ids(
#if (!defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)) || defined(AMD_WMMA_AVAILABLE)
#if defined(AMD_WMMA_AVAILABLE)
// Special case for tf32, just dummy mma layout as wmma doesn't support it.
constexpr int tile_B_I = std::is_same_v<T, float> ? 8 : 16;
constexpr int tile_C_J = std::is_same_v<T, float> ? 8 : 16;
typedef tile<16, 8, T> tile_A;
typedef tile<tile_B_I, 8, T> tile_B;
typedef tile<16, tile_C_J, float> tile_C;
constexpr bool is_tf32 = std::is_same_v<T, float>;
constexpr int tile_B_I = is_tf32 ? 8 : 16;
constexpr int tile_C_J = is_tf32 ? 8 : 16;
constexpr data_layout ab_layout = is_tf32 ? DATA_LAYOUT_I_MAJOR : get_input_data_layout();
typedef tile<16, 8, T, ab_layout> tile_A;
typedef tile<tile_B_I, 8, T, ab_layout> tile_B;
typedef tile<16, tile_C_J, float, DATA_LAYOUT_J_MAJOR> tile_C;
#else
#ifdef VOLTA_MMA_AVAILABLE
if constexpr (!std::is_same_v<T, half2>) {NO_DEVICE_CODE;} else {

65
ggml/src/ggml-cuda/mmq.cuh
View File

@ -797,9 +797,10 @@ template <int mmq_x, int mmq_y, mmq_q8_1_ds_layout ds_layout>
static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) {
#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
typedef tile<16, 8, int> tile_A;
typedef tile<16, 8, int> tile_B;
typedef tile<16, 16, int> tile_C;
constexpr data_layout input_layout = get_input_data_layout();
typedef tile<16, 8, int, input_layout> tile_A;
typedef tile<16, 8, int, input_layout> tile_B;
typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = granularity;
@ -966,9 +967,10 @@ template <int mmq_x, int mmq_y>
static __device__ __forceinline__ void vec_dot_q8_1_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) {
#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
typedef tile<16, 8, int> tile_A;
typedef tile<16, 8, int> tile_B;
typedef tile<16, 16, int> tile_C;
constexpr data_layout input_layout = get_input_data_layout();
typedef tile<16, 8, int, input_layout> tile_A;
typedef tile<16, 8, int, input_layout> tile_B;
typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = granularity;
@ -1130,10 +1132,11 @@ template <int mmq_x, int mmq_y>
static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) {
#if defined(AMD_MFMA_AVAILABLE)
typedef tile<16, 8, int> tile_A;
typedef tile<16, 8, int> tile_B;
typedef tile<16, 16, int> tile_C;
typedef tile<64, 2, int> tile_load;
constexpr data_layout input_layout = get_input_data_layout();
typedef tile<16, 8, int, input_layout> tile_A;
typedef tile<16, 8, int, input_layout> tile_B;
typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
typedef tile<64, 2, int, input_layout> tile_load;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = granularity;
@ -1179,9 +1182,10 @@ static __device__ __forceinline__ void vec_dot_q8_0_16_q8_1_mma(
}
}
#elif defined(AMD_WMMA_AVAILABLE) //wmma instructions can handle 16x4 tiles, does not require loading 64x2 tiles
typedef tile<16, 4, int> tile_A;
typedef tile<16, 4, int> tile_B;
typedef tile<16, 16, int> tile_C;
constexpr data_layout input_layout = get_input_data_layout();
typedef tile<16, 4, int, input_layout> tile_A;
typedef tile<16, 4, int, input_layout> tile_B;
typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = granularity;
@ -1435,10 +1439,11 @@ template <int mmq_x, int mmq_y>
static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) {
#if defined(AMD_MFMA_AVAILABLE)
typedef tile<16, 8, int> tile_A;
typedef tile<16, 8, int> tile_B;
typedef tile<16, 16, int> tile_C;
typedef tile<64, 2, int> tile_load;
constexpr data_layout input_layout = get_input_data_layout();
typedef tile<16, 8, int, input_layout> tile_A;
typedef tile<16, 8, int, input_layout> tile_B;
typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
typedef tile<64, 2, int, input_layout> tile_load;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = granularity;
@ -1501,10 +1506,10 @@ static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma(
}
}
#elif defined(AMD_WMMA_AVAILABLE) //wmma instructions can handle 16x4 tiles, does not require loading 64x2 tiles
typedef tile<16, 4, int> tile_A;
typedef tile<16, 4, int> tile_B;
typedef tile<16, 16, int> tile_C;
constexpr data_layout input_layout = get_input_data_layout();
typedef tile<16, 4, int, input_layout> tile_A;
typedef tile<16, 4, int, input_layout> tile_B;
typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = granularity;
@ -2265,10 +2270,11 @@ template <int mmq_x, int mmq_y>
static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
const int * __restrict__ x, const int * __restrict__ y, float * __restrict__ sum, const int k00) {
#if defined(AMD_MFMA_AVAILABLE)
typedef tile<16, 8, int> tile_A;
typedef tile<16, 8, int> tile_B;
typedef tile<16, 16, int> tile_C;
typedef tile<64, 2, int> tile_load;
constexpr data_layout input_layout = get_input_data_layout();
typedef tile<16, 8, int, input_layout> tile_A;
typedef tile<16, 8, int, input_layout> tile_B;
typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
typedef tile<64, 2, int, input_layout> tile_load;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = granularity;
@ -2316,9 +2322,10 @@ static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma(
}
}
#elif defined(AMD_WMMA_AVAILABLE) //wmma instructions can handle 16x4 tiles, does not require loading 64x2 tiles
typedef tile<16, 4, int> tile_A;
typedef tile<16, 4, int> tile_B;
typedef tile<16, 16, int> tile_C;
constexpr data_layout input_layout = get_input_data_layout();
typedef tile<16, 4, int, input_layout> tile_A;
typedef tile<16, 4, int, input_layout> tile_B;
typedef tile<16, 16, int, DATA_LAYOUT_J_MAJOR> tile_C;
constexpr int granularity = mmq_get_granularity_device(mmq_x);
constexpr int rows_per_warp = granularity;
@ -3015,7 +3022,7 @@ static __device__ __forceinline__ void mmq_write_back_mma(
#if defined(AMD_MFMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
constexpr int tileC_IJ = mmq_get_granularity_device(0);
typedef tile<tileC_IJ, tileC_IJ, int> tile_C;
typedef tile<tileC_IJ, tileC_IJ, int, DATA_LAYOUT_J_MAJOR> tile_C;
constexpr int rows_per_warp = granularity;
#else
typedef tile<16, 8, int> tile_C;

34
ggml/src/ggml-cuda/ssm-conv.cu
View File

@ -102,31 +102,25 @@ static void ssm_conv_f32_cuda(const float * src0, const float * src1, const int
const int threads = 128;
GGML_ASSERT(nr % threads == 0);
if (n_t <= 32) {
const dim3 blocks(n_s, (nr + threads - 1) / threads, 1);
if (nc == 4) {
ssm_conv_f32<threads, 4><<<blocks, threads, 0, stream>>>(src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1,
dst, dst_nb0, dst_nb1, dst_nb2, n_t);
} else if (nc == 3) {
ssm_conv_f32<threads, 3><<<blocks, threads, 0, stream>>>(src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1,
dst, dst_nb0, dst_nb1, dst_nb2, n_t);
auto launch_kernel = [&](auto NC) {
constexpr int kNC = decltype(NC)::value;
if (n_t <= 32) {
const dim3 blocks(n_s, (nr + threads - 1) / threads, 1);
ssm_conv_f32<threads, kNC><<<blocks, threads, 0, stream>>>(src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1,
dst, dst_nb0, dst_nb1, dst_nb2, n_t);
} else {
GGML_ABORT("Only support kernel size = 3 or size = 4 right now.");
}
} else {
if (nc == 4) {
const int64_t split_n_t = 32;
dim3 blocks(n_s, (nr + threads - 1) / threads, (n_t + split_n_t - 1) / split_n_t);
ssm_conv_long_token_f32<threads, 4, split_n_t><<<blocks, threads, 0, stream>>>(
ssm_conv_long_token_f32<threads, kNC, split_n_t><<<blocks, threads, 0, stream>>>(
src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t);
} else if (nc == 3) {
const int64_t split_n_t = 32;
dim3 blocks(n_s, (nr + threads - 1) / threads, (n_t + split_n_t - 1) / split_n_t);
ssm_conv_long_token_f32<threads, 3, split_n_t><<<blocks, threads, 0, stream>>>(
src0, src1, src0_nb0, src0_nb1, src0_nb2, src1_nb1, dst, dst_nb0, dst_nb1, dst_nb2, n_t);
} else {
GGML_ABORT("Only support kernel size = 3 or size = 4 right now.");
}
};
switch (nc) {
case 3: launch_kernel(std::integral_constant<int, 3>{}); break;
case 4: launch_kernel(std::integral_constant<int, 4>{}); break;
case 9: launch_kernel(std::integral_constant<int, 9>{}); break;
default: GGML_ABORT("Only support kernel sizes 3, 4, 9 right now.");
}
}

19
ggml/src/ggml-cuda/topk-moe.cu
View File

@ -268,7 +268,23 @@ void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx,
}
}
bool ggml_cuda_should_use_topk_moe(const ggml_tensor * softmax, const ggml_tensor * weights, const ggml_tensor * clamp) {
bool ggml_cuda_should_use_topk_moe(const ggml_tensor * softmax,
const ggml_tensor * weights,
const ggml_tensor * get_rows,
const ggml_tensor * argsort,
const ggml_tensor * clamp,
int n_expert) {
ggml_tensor * probs = get_rows->src[0];
if (probs->op != GGML_OP_RESHAPE) {
return false;
}
probs = probs->src[0];
ggml_tensor * selection_probs = argsort->src[0];
if (probs != selection_probs) {
return false;
}
float scale = 1.0f;
float max_bias = 0.0f;
@ -288,7 +304,6 @@ bool ggml_cuda_should_use_topk_moe(const ggml_tensor * softmax, const ggml_tenso
return false;
}
const int n_expert = softmax->ne[0];
// n_expert must be a power of 2
if ((n_expert & (n_expert - 1)) != 0 || n_expert > 512) {
return false;

7
ggml/src/ggml-cuda/topk-moe.cuh
View File

@ -11,6 +11,11 @@ void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx,
const bool delayed_softmax = false,
ggml_tensor * weight_clamp = nullptr);
bool ggml_cuda_should_use_topk_moe(const ggml_tensor * softmax, const ggml_tensor * weights, const ggml_tensor * clamp = nullptr);
bool ggml_cuda_should_use_topk_moe(const ggml_tensor * softmax,
const ggml_tensor * weights,
const ggml_tensor * get_rows,
const ggml_tensor * argsort,
const ggml_tensor * clamp,
int n_expert);
std::initializer_list<enum ggml_op> ggml_cuda_topk_moe_ops(bool with_norm, bool delayed_softmax = false);

4
ggml/src/ggml-hexagon/CMakeLists.txt
View File

@ -2,6 +2,7 @@ include(${HEXAGON_SDK_ROOT}/build/cmake/hexagon_fun.cmake)
include(ExternalProject)
option(GGML_HEXAGON_HTP_DEBUG "ggml-hexagon: enable HTP debug output" OFF)
set(GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE 128 CACHE STRING "ggml-hexagon: quantize group size (32, 64, or 128)")
add_library(htp_iface OBJECT
${CMAKE_CURRENT_BINARY_DIR}/htp_iface_stub.c)
@ -41,7 +42,8 @@ set(HTP_CMAKE_ARGS
-DCMAKE_INSTALL_LIBDIR=${CMAKE_CURRENT_BINARY_DIR}
-DHEXAGON_SDK_ROOT=$ENV{HEXAGON_SDK_ROOT}
-DHEXAGON_TOOLS_ROOT=$ENV{HEXAGON_TOOLS_ROOT}
-DHEXAGON_HTP_DEBUG=${GGML_HEXAGON_HTP_DEBUG})
-DHEXAGON_HTP_DEBUG=${GGML_HEXAGON_HTP_DEBUG}
-DGGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE})
ExternalProject_Add(htp-v68
SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/htp BUILD_ALWAYS ON

23
ggml/src/ggml-hexagon/ggml-hexagon.cpp
View File

@ -2161,8 +2161,14 @@ static bool ggml_hexagon_supported_activations(const struct ggml_hexagon_session
}
// src0, src1 & dst must be mapped to the same session
if (!hex_supported_buffer(sess, src0, src1, dst)) {
return false;
if(src1){
if (!hex_supported_buffer(sess, src0, src1, dst)) {
return false;
}
}else{
if (!hex_supported_buffer(sess, src0, dst)) {
return false;
}
}
return true;
@ -2662,6 +2668,10 @@ static void ggml_hexagon_unary(const struct ggml_tensor * op, uint32_t flags) {
req.op = HTP_OP_UNARY_SILU;
supported = true;
}
else if (ggml_get_unary_op(dst) == GGML_UNARY_OP_GELU){
req.op = HTP_OP_UNARY_GELU;
supported = true;
}
break;
case GGML_OP_GLU:
@ -2677,6 +2687,7 @@ static void ggml_hexagon_unary(const struct ggml_tensor * op, uint32_t flags) {
case GGML_OP_SOFT_MAX:
req.op = HTP_OP_SOFTMAX;
supported = true;
break;
default:
break;
@ -2956,6 +2967,8 @@ static ggml_status ggml_backend_hexagon_graph_compute(ggml_backend_t backend, gg
case GGML_OP_UNARY:
if (ggml_get_unary_op(node) == GGML_UNARY_OP_SILU) {
ggml_hexagon_unary(node, flags);
} else if (ggml_get_unary_op(node) == GGML_UNARY_OP_GELU) {
ggml_hexagon_unary(node, flags);
}
break;
case GGML_OP_GLU:
@ -3254,7 +3267,6 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
auto sess = static_cast<ggml_hexagon_session *>(dev->context);
bool supp = false;
switch (op->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
@ -3294,10 +3306,13 @@ static bool ggml_backend_hexagon_device_supports_op(ggml_backend_dev_t dev, cons
if (ggml_get_unary_op(op) == GGML_UNARY_OP_SILU) {
supp = ggml_hexagon_supported_activations(sess, op);
}
else if (ggml_get_unary_op(op) == GGML_UNARY_OP_GELU){
supp = ggml_hexagon_supported_activations(sess, op);
}
break;
case GGML_OP_GLU:
if ((ggml_get_glu_op(op) == GGML_GLU_OP_SWIGLU) /* || (ggml_get_glu_op(op) == GGML_GLU_OP_SWIGLU_OAI) */) {
if ((ggml_get_glu_op(op) == GGML_GLU_OP_SWIGLU) || (ggml_get_glu_op(op) == GGML_GLU_OP_SWIGLU_OAI) ) {
supp = ggml_hexagon_supported_activations(sess, op);
}
break;

3
ggml/src/ggml-hexagon/htp/CMakeLists.txt
View File

@ -31,7 +31,8 @@ add_library(${HTP_LIB} SHARED
)
target_compile_definitions(${HTP_LIB} PRIVATE
$<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,HTP_DEBUG=1,NDEBUG=1>)
$<IF:$<BOOL:${HEXAGON_HTP_DEBUG}>,HTP_DEBUG=1,NDEBUG=1>
FP32_QUANTIZE_GROUP_SIZE=${GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE})
build_idl(htp_iface.idl ${HTP_LIB})

92
ggml/src/ggml-hexagon/htp/act-ops.c
View File

@ -231,7 +231,7 @@ static void glu_swiglu_oai_fp32_per_thread(const struct htp_tensor * src0,
// x (src0_spad_data) = std::min(src0_p[k], limit);
hvx_min_scalar_f32((const uint8_t *) src0, limit, src0_spad_data, nc);
// y1 (src1_spad_data) = std::clamp(src1_p[k], -limit, limit);
hvx_clamp_scalar_f32((const uint8_t *) src1, limit, limit, src1_spad_data, nc);
hvx_clamp_scalar_f32((const uint8_t *) src1, -limit, limit, src1_spad_data, nc);
// y (src1_spad_data) = y1 + 1.f
hvx_add_scalar_f32(src1_spad_data, 1.0, src1_spad_data, nc);
// x1 (dst_spad_data) = alpha * (x)
@ -255,6 +255,91 @@ static void glu_swiglu_oai_fp32_per_thread(const struct htp_tensor * src0,
src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
}
static void unary_gelu_fp32_per_thread(const struct htp_tensor * src0,
struct htp_tensor * dst,
const int32_t * op_params,
struct htp_spad * src0_spad,
struct htp_spad * dst_spad,
uint32_t nth,
uint32_t ith,
uint32_t src0_nrows_per_thread) {
htp_act_preamble2;
uint64_t t1, t2;
t1 = HAP_perf_get_qtimer_count();
const size_t src0_row_size = nb01;
const size_t dst_row_size = nb1;
const uint32_t src0_nrows = ne01 * ne02 * ne03;
const uint32_t src0_start_row = src0_nrows_per_thread * ith;
const uint32_t src0_end_row = MIN(src0_start_row + src0_nrows_per_thread, src0_nrows);
// no work for this thread
if (src0_start_row >= src0_end_row) {
return;
}
int is_aligned = 1;
int opt_path = 0;
if (!htp_is_aligned((void *) src0->data, VLEN) || !htp_is_aligned((void *) dst->data, VLEN)) {
is_aligned = 0;
FARF(HIGH, "silu-f32: unaligned addresses in elementwise op, possibly slower execution\n");
}
if ((1 == is_aligned) && !(nb01 & (VLEN - 1))) {
opt_path = 1;
}
const uint8_t * restrict data_src0 = (const uint8_t *) src0->data;
uint8_t * restrict data_dst = (uint8_t *) dst->data;
uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_row_size);
uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_row_size);
const int BLOCK = 8;
for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) {
const uint32_t block_end = MIN(ir + BLOCK, src0_end_row);
// Prefetch next block
if (block_end < src0_end_row) {
const float * restrict prefetch_ptr = (float *) (data_src0 + (block_end * src0_row_size));
htp_l2fetch(prefetch_ptr, 1, block_end * src0_row_size, src0_row_size);
}
// Process rows in current block
for (uint32_t ib = ir; ib < block_end; ib++) {
const float * restrict src0 = (float *) (data_src0 + (ib * src0_row_size));
float * restrict dst = (float *) (data_dst + (ib * dst_row_size));
// gelu = x * sigmoid(1.702 * x) // current implementation
if (1 == opt_path) {
hvx_mul_scalar_f32((const uint8_t *) src0, (float) 1.702, (uint8_t *) src0_spad_data, ne0);
hvx_fast_sigmoid_f32((const uint8_t *) src0_spad_data, (uint8_t *) src0_spad_data, ne0);
hvx_mul_f32_opt((const uint8_t *) src0, src0_spad_data, (uint8_t *) dst, ne0);
} else {
hvx_mul_scalar_f32( (const uint8_t *) src0, (float)1.702, (uint8_t *) src0_spad_data, ne0);
hvx_sigmoid_f32((const uint8_t *) src0_spad_data, (uint8_t *) src0_spad_data, ne0);
hvx_mul_f32((const uint8_t *) src0, src0_spad_data, (uint8_t *) dst, ne0);
}
}
}
t2 = HAP_perf_get_qtimer_count();
FARF(HIGH, "gelu-f32 %d/%d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, opt_path, ne00, ne01, ne02,
ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
}
static void unary_gelu_fp32(unsigned int n, unsigned int i, void * data) {
struct htp_ops_context * octx = (struct htp_ops_context *) data;
unary_gelu_fp32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i,
octx->src0_nrows_per_thread);
}
static void unary_silu_fp32_per_thread(const struct htp_tensor * src0,
struct htp_tensor * dst,
const int32_t * op_params,
@ -371,7 +456,10 @@ static int execute_op_activations_fp32(struct htp_ops_context * octx) {
act_op_func = glu_swiglu_oai_fp32;
op_type = "swiglu-oai-f32";
break;
case HTP_OP_UNARY_GELU:
act_op_func = unary_gelu_fp32;
op_type = "gelu-f32";
break;
default:
FARF(ERROR, "Unsupported activations Op %u\n", octx->op);
return HTP_STATUS_NO_SUPPORT;

11
ggml/src/ggml-hexagon/htp/htp-msg.h
View File

@ -51,11 +51,12 @@ enum htp_op {
HTP_OP_MUL_MAT_ID = 5,
HTP_OP_RMS_NORM = 6,
HTP_OP_UNARY_SILU = 7,
HTP_OP_GLU_SWIGLU = 8,
HTP_OP_GLU_SWIGLU_OAI = 9,
HTP_OP_SOFTMAX = 10,
HTP_OP_ADD_ID = 11,
HTP_OP_ROPE = 12,
HTP_OP_UNARY_GELU = 8,
HTP_OP_GLU_SWIGLU = 9,
HTP_OP_GLU_SWIGLU_OAI = 10,
HTP_OP_SOFTMAX = 11,
HTP_OP_ADD_ID = 12,
HTP_OP_ROPE = 13,
INVALID
};

201
ggml/src/ggml-hexagon/htp/hvx-utils.c
View File

@ -49,6 +49,8 @@ void hvx_mul_f32(const uint8_t * restrict src0,
FARF(HIGH, "hvx_mul_f32: unaligned loop in hvx op, possibly slower execution\n");
}
bool handled_leftover = false;
if (0 == unaligned_loop) {
HVX_Vector * restrict vec_in1 = (HVX_Vector *) src0;
HVX_Vector * restrict vec_in2 = (HVX_Vector *) src1;
@ -60,18 +62,59 @@ void hvx_mul_f32(const uint8_t * restrict src0,
*vec_out++ = Q6_Vsf_equals_Vqf32(v);
}
} else {
int step_of_1 = num_elems_whole >> 5; // divby 32, because 32 float = 128 bytes per HVX vector
int leftover_size = left_over * sizeof(float);
HVX_Vector * restrict vec_in1 = (HVX_Vector *) src0;
HVX_Vector * restrict vec_in2 = (HVX_Vector *) src1;
HVX_UVector * restrict vec_out = (HVX_UVector *) dst;
HVX_Vector slinep;
HVX_Vector slinec;
HVX_Vector sline;
HVX_Vector sline2p;
HVX_Vector sline2c;
HVX_Vector sline2;
slinep = *vec_in1++;
sline2p = *vec_in2++;
#pragma unroll(4)
for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
HVX_Vector in1 = *(HVX_UVector *) (src0 + i * SIZEOF_FP32);
HVX_Vector in2 = *(HVX_UVector *) (src1 + i * SIZEOF_FP32);
for (int i = step_of_1 - 1; i > 0; i--) {
slinec = *vec_in1++;
sline2c = *vec_in2++;
sline = Q6_V_valign_VVR(slinec, slinep, (size_t) src0);
sline2 = Q6_V_valign_VVR(sline2c, sline2p, (size_t) src1);
HVX_Vector out = Q6_Vqf32_vmpy_VsfVsf(in1, in2);
*((HVX_UVector *) (vec_out++)) = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(sline, sline2));
slinep = slinec;
sline2p = sline2c;
}
if (step_of_1 > 1) {
slinec = htp_is_aligned(vec_in1, VLEN) && left_over == 0 ? slinep : *vec_in1++;
sline2c = htp_is_aligned(vec_in2, VLEN) && left_over == 0 ? sline2p : *vec_in2++;
*(HVX_UVector *) (dst + i * SIZEOF_FP32) = Q6_Vsf_equals_Vqf32(out);
sline = Q6_V_valign_VVR(slinec, slinep, (size_t) src0);
sline2 = Q6_V_valign_VVR(sline2c, sline2p, (size_t) src1);
*((HVX_UVector *) (vec_out++)) = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(sline, sline2));
slinep = slinec;
sline2p = sline2c;
}
if (left_over > 0) {
slinec = (is_in_one_chunk(vec_in1, leftover_size, VLEN) ? slinep : *vec_in1++);
sline = Q6_V_valign_VVR(slinec, slinep, (size_t) src0);
sline2c = (is_in_one_chunk(vec_in2, leftover_size, VLEN) ? sline2p : *vec_in2++);
sline2 = Q6_V_valign_VVR(sline2c, sline2p, (size_t) src1);
HVX_Vector out = Q6_Vqf32_vmpy_VsfVsf(sline, sline2);
hvx_vec_store_u(vec_out, leftover_size, Q6_Vsf_equals_Vqf32(out));
handled_leftover = true;
}
}
if (left_over > 0) {
if (left_over > 0 && !handled_leftover) {
const float * src0f = (const float *) src0 + num_elems_whole;
const float * src1f = (const float *) src1 + num_elems_whole;
float * dstf = (float *) dst + num_elems_whole;
@ -464,7 +507,7 @@ void hvx_mul_scalar_f32(const uint8_t * restrict src, const float val, uint8_t *
}
HVX_Vector val_vec = hvx_vec_splat_fp32(val);
bool handled_leftover = false;
if (0 == unaligned_loop) {
HVX_Vector * restrict vec_in1 = (HVX_Vector *) src;
HVX_Vector * restrict vec_out = (HVX_Vector *) dst;
@ -475,17 +518,47 @@ void hvx_mul_scalar_f32(const uint8_t * restrict src, const float val, uint8_t *
*vec_out++ = Q6_Vsf_equals_Vqf32(v);
}
} else {
int step_of_1 = num_elems >> 5; // divby 32, because 32 float = 128 bytes per HVX vector
int leftover_size = left_over * sizeof(float);
HVX_Vector * input_v_ptr = (HVX_Vector *) src;
HVX_UVector * output_v_ptr = (HVX_UVector *) dst;
HVX_Vector slinep;
HVX_Vector slinec;
HVX_Vector sline;
slinep = *input_v_ptr++;
#pragma unroll(4)
for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
HVX_Vector in = *(HVX_UVector *) (src + i * SIZEOF_FP32);
for (int i = step_of_1 - 1; i > 0; i--) {
slinec = *input_v_ptr++;
sline = Q6_V_valign_VVR(slinec, slinep, (size_t) src);
*((HVX_UVector *) (output_v_ptr++)) = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(sline, val_vec));
/* Prepare slinep for next iteration */
slinep = slinec;
}
HVX_Vector out = Q6_Vqf32_vmpy_VsfVsf(in, val_vec);
if (step_of_1 > 0) {
slinec = htp_is_aligned(input_v_ptr, VLEN) && left_over == 0 ? slinep : *input_v_ptr++;
sline = Q6_V_valign_VVR(slinec, slinep, (size_t) src);
*((HVX_UVector *) (output_v_ptr++)) = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(sline, val_vec));
*(HVX_UVector *) (dst + i * SIZEOF_FP32) = Q6_Vsf_equals_Vqf32(out);
slinep = slinec;
}
if (leftover_size > 0) {
slinec = (is_in_one_chunk(input_v_ptr, leftover_size, VLEN) ? slinep : *input_v_ptr++);
sline = Q6_V_valign_VVR(slinec, slinep, (size_t) src);
HVX_Vector sout = Q6_Vsf_equals_Vqf32(Q6_Vqf32_vmpy_VsfVsf(sline, val_vec));
hvx_vec_store_u(output_v_ptr, leftover_size, sout);
handled_leftover = true;
}
}
if (left_over > 0) {
if (left_over > 0 && !handled_leftover) {
const float * srcf = (const float *) src + num_elems_whole;
float * dstf = (float *) dst + num_elems_whole;
@ -875,35 +948,45 @@ float hvx_self_max_f32(const uint8_t * restrict src, const int num_elems) {
void hvx_min_scalar_f32(const uint8_t * restrict src, const float val, uint8_t * restrict dst, const int num_elems) {
size_t left_over = num_elems & (VLEN_FP32 - 1);
size_t num_elems_whole = num_elems - left_over;
int unalign_address = 0;
if ((0 == htp_is_aligned((void *) src, VLEN)) || (0 == htp_is_aligned((void *) dst, VLEN))) {
FARF(HIGH, "hvx_min_scalar_f32: unaligned address in hvx op, possibly slower execution\n");
unalign_address = 1;
}
assert((1 == htp_is_aligned((void *) src, VLEN)) || (0 == num_elems_whole));
const float * src_f = (const float *) src;
HVX_Vector vec_min = Q6_V_vsplat_R(val);
HVX_Vector vec_min = hvx_vec_splat_fp32(val);
HVX_Vector * restrict vec_in = (HVX_Vector *) src;
HVX_Vector * restrict vec_out = (HVX_Vector *) dst;
if(unalign_address == 0){
HVX_Vector * restrict vec_in = (HVX_Vector *) src;
HVX_Vector * restrict vec_out = (HVX_Vector *) dst;
#pragma unroll(4)
for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
vec_min = Q6_Vsf_vmin_VsfVsf(vec_min, *vec_in++);
*vec_out++ = Q6_Vsf_equals_Vqf32(vec_min);
#pragma unroll(4)
for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
HVX_Vector min_clamp = Q6_Vsf_vmin_VsfVsf(vec_min, *vec_in++);
*vec_out++ = (min_clamp);
}
}else{
HVX_UVector * restrict vec_in = (HVX_Vector *) src;
HVX_UVector * restrict vec_out = (HVX_Vector *) dst;
#pragma unroll(4)
for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
HVX_Vector min_clamp = Q6_Vsf_vmin_VsfVsf(vec_min, *vec_in++);
*vec_out++ = (min_clamp);
}
}
if (left_over > 0) {
if (left_over > 0 ) {
const float * srcf = (const float *) src + num_elems_whole;
float * dstf = (float *) dst + num_elems_whole;
HVX_Vector in = *(HVX_UVector *) srcf;
HVX_UVector in = *(HVX_UVector *) srcf;
vec_min = Q6_Vsf_vmin_VsfVsf(vec_min, in);
HVX_UVector min_clamp = Q6_Vsf_vmin_VsfVsf(vec_min, in);
hvx_vec_store_u((void *) dstf, left_over * SIZEOF_FP32, Q6_Vsf_equals_Vqf32(vec_min));
hvx_vec_store_u((void *) dstf, left_over * SIZEOF_FP32, (min_clamp));
}
}
@ -915,46 +998,70 @@ void hvx_clamp_scalar_f32(const uint8_t * restrict src,
size_t left_over = num_elems & (VLEN_FP32 - 1);
size_t num_elems_whole = num_elems - left_over;
int unalign_address = 0;
if ((0 == htp_is_aligned((void *) src, VLEN)) || (0 == htp_is_aligned((void *) dst, VLEN))) {
FARF(HIGH, "hvx_clamp_scalar_f32: unaligned address in hvx op, possibly slower execution\n");
unalign_address = 1;
}
assert((1 == htp_is_aligned((void *) src, VLEN)) || (0 == num_elems_whole));
HVX_Vector * restrict vec_in = (HVX_Vector *) src;
HVX_Vector * restrict vec_out = (HVX_Vector *) dst;
HVX_Vector range_left = hvx_vec_splat_fp32(limit_left);
HVX_Vector range_right = hvx_vec_splat_fp32(limit_right);
#pragma unroll(4)
for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
HVX_Vector in_vec = *vec_in++;
HVX_Vector temp_v = in_vec;
if(unalign_address == 0){
HVX_Vector * restrict vec_in = (HVX_Vector *) src;
HVX_Vector * restrict vec_out = (HVX_Vector *) dst;
HVX_VectorPred pred_cap_right = Q6_Q_vcmp_gt_VsfVsf(in_vec, range_right);
HVX_VectorPred pred_cap_left = Q6_Q_vcmp_gt_VsfVsf(range_left, in_vec);
in_vec = Q6_V_vmux_QVV(pred_cap_right, range_right, temp_v);
in_vec = Q6_V_vmux_QVV(pred_cap_left, range_left, temp_v);
*vec_out++ = Q6_Vsf_equals_Vqf32(in_vec);
#pragma unroll(4)
for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
HVX_Vector in_vec = *vec_in++;
HVX_Vector temp_v = in_vec;
HVX_VectorPred pred_cap_right = Q6_Q_vcmp_gt_VsfVsf(in_vec, range_right);
HVX_VectorPred pred_cap_left = Q6_Q_vcmp_gt_VsfVsf(range_left, in_vec);
in_vec = Q6_V_vmux_QVV(pred_cap_right, range_right, temp_v);
in_vec = Q6_V_vmux_QVV(pred_cap_left, range_left, in_vec);
*vec_out++ = in_vec;
}
}else{
HVX_UVector * restrict vec_in = (HVX_UVector *) src;
HVX_UVector * restrict vec_out = (HVX_UVector *) dst;
#pragma unroll(4)
for (int i = 0; i < num_elems_whole; i += VLEN_FP32) {
HVX_Vector in_vec = *vec_in++;
HVX_Vector temp_v = in_vec;
HVX_VectorPred pred_cap_right = Q6_Q_vcmp_gt_VsfVsf(in_vec, range_right);
HVX_VectorPred pred_cap_left = Q6_Q_vcmp_gt_VsfVsf(range_left, in_vec);
in_vec = Q6_V_vmux_QVV(pred_cap_right, range_right, temp_v);
in_vec = Q6_V_vmux_QVV(pred_cap_left, range_left, in_vec);
*vec_out++ = in_vec;
}
}
if (left_over > 0) {
const float * srcf = (const float *) src + num_elems_whole;
float * dstf = (float *) dst + num_elems_whole;
HVX_Vector in = *(HVX_UVector *) srcf;
HVX_Vector in_vec = *(HVX_UVector *) srcf;
HVX_Vector temp_v = in;
HVX_Vector temp_v = in_vec;
HVX_VectorPred pred_cap_right = Q6_Q_vcmp_gt_VsfVsf(in, range_right);
HVX_VectorPred pred_cap_left = Q6_Q_vcmp_gt_VsfVsf(range_left, in);
HVX_VectorPred pred_cap_right = Q6_Q_vcmp_gt_VsfVsf(in_vec, range_right);
HVX_VectorPred pred_cap_left = Q6_Q_vcmp_gt_VsfVsf(range_left, in_vec);
in = Q6_V_vmux_QVV(pred_cap_right, range_right, temp_v);
in = Q6_V_vmux_QVV(pred_cap_left, range_left, temp_v);
in_vec = Q6_V_vmux_QVV(pred_cap_right, range_right, temp_v);
in_vec = Q6_V_vmux_QVV(pred_cap_left, range_left, in_vec);
hvx_vec_store_u((void *) dstf, left_over * SIZEOF_FP32, Q6_Vsf_equals_Vqf32(in));
hvx_vec_store_u((void *) dstf, left_over * SIZEOF_FP32, in_vec);
}
}

57
ggml/src/ggml-hexagon/htp/hvx-utils.h
View File

@ -265,12 +265,16 @@ static inline void hvx_bcast_fp32_a(uint8_t * restrict dst, float elem, uint32_t
}
}
/* Return whether 'n' elements from vector are in the one chunk of 'chunk_size'. */
static __attribute__((always_inline)) int32_t is_in_one_chunk(void * addr, uint32_t n, uint32_t chunk_size) {
uint32_t left_off = (size_t) addr & (chunk_size - 1);
uint32_t right_off = left_off + n;
return right_off <= chunk_size;
}
static void hvx_vec_dump_fp16_n(char * pref, HVX_Vector v, uint32_t n) {
HVX_VectorAlias u = { .v = v };
@ -994,6 +998,59 @@ static inline void hvx_fast_sigmoid_f32(const uint8_t * restrict src, uint8_t *
}
}
static inline void hvx_sigmoid_f32(const uint8_t * restrict src, uint8_t * restrict dst, const int num_elems){
int step_of_1 = num_elems >> 5; // divby 32, because 32 float = 128 bytes per HVX vector
int leftover = num_elems - (step_of_1 * VLEN_FP32);
int32_t leftover_size = leftover * sizeof(float);
static const float kMinExp = -87.f; // 0
static const float kMaxExp = 87.f; // 1
const HVX_Vector one = hvx_vec_splat_fp32(1.f);
const HVX_Vector max_exp = hvx_vec_splat_fp32(kMaxExp);
const HVX_Vector min_exp = hvx_vec_splat_fp32(kMinExp);
const float *input = (float *)src;
float *output = (float *)dst;
HVX_Vector * input_v_ptr = (HVX_Vector *) input;
HVX_UVector * output_v_ptr = (HVX_UVector *) output;
HVX_Vector slinep;
HVX_Vector slinec;
HVX_Vector sline;
slinep = *input_v_ptr++;
#pragma unroll(4)
for (int i = step_of_1 - 1; i > 0; i--) {
slinec = *input_v_ptr++;
sline = Q6_V_valign_VVR(slinec, slinep, (size_t) input);
*((HVX_UVector *) (output_v_ptr++)) = hvx_vec_fast_sigmoid_fp32_guard(sline, one, max_exp, min_exp);
/* Prepare slinep for next iteration */
slinep = slinec;
}
if (step_of_1 > 0) {
slinec = htp_is_aligned(input_v_ptr, 128) && leftover == 0 ? slinep : *input_v_ptr++;
sline = Q6_V_valign_VVR(slinec, slinep, (size_t) input);
*((HVX_UVector *) (output_v_ptr++)) = hvx_vec_fast_sigmoid_fp32_guard(sline, one, max_exp, min_exp);
;
slinep = slinec;
}
if (leftover > 0) {
slinec = (is_in_one_chunk(input_v_ptr, leftover_size, 128) ? slinep : *input_v_ptr++);
sline = Q6_V_valign_VVR(slinec, slinep, (size_t) input);
HVX_Vector sout = hvx_vec_fast_sigmoid_fp32_guard(sline, one, max_exp, min_exp);
hvx_vec_store_u(output_v_ptr, leftover_size, sout);
}
}
float hvx_sum_of_squares_f32(const uint8_t * restrict src, const int num_elems);
void hvx_mul_f32(const uint8_t * restrict src0,
const uint8_t * restrict src1,

2
ggml/src/ggml-hexagon/htp/main.c
View File

@ -798,6 +798,7 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
break;
case HTP_OP_UNARY_SILU:
case HTP_OP_UNARY_GELU:
if (n_bufs != 2) {
FARF(ERROR, "Bad act-req buffer list");
continue;
@ -806,6 +807,7 @@ static void htp_packet_callback(dspqueue_t queue, int error, void * context) {
break;
case HTP_OP_GLU_SWIGLU:
case HTP_OP_GLU_SWIGLU_OAI:
case HTP_OP_SOFTMAX:
if ((n_bufs != 2) && (n_bufs != 3)) {
FARF(ERROR, "Bad act-req buffer list");

139
ggml/src/ggml-hexagon/htp/matmul-ops.c
View File

@ -92,6 +92,18 @@ static const uint8_t __attribute__((aligned(128))) repl_1x_fp16[128] = {
0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
};
// vdelta control to replicate first fp16 value across all elements
static const uint8_t __attribute__((aligned(128))) repl_2x_fp16[128] = {
0x00, 0x00, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
0x00, 0x00, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
0x20, 0x20, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
0x10, 0x10, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02, 0x08, 0x08, 0x02, 0x02, 0x04, 0x04, 0x02, 0x02,
};
// vdelta control to expand first 32 e8m0 values into 32 uint32 elements
static const uint8_t __attribute__((aligned(128))) expand_x32_e8m0[128] = {
0x00, 0x00, 0x00, 0x00, 0x01, 0x04, 0x00, 0x00, 0x02, 0x00, 0x08, 0x08, 0x01, 0x02, 0x00, 0x04, 0x04, 0x00, 0x00,
@ -1594,6 +1606,118 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0,
// *** dynamic quant
static inline void quantize_block_fp32_q8x1(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
assert((unsigned long) x % 128 == 0);
assert((unsigned long) y_q % 128 == 0);
HVX_Vector * vx = (HVX_Vector *) x;
HVX_Vector zero = Q6_V_vsplat_R(0);
// Use reduce max fp32 to find max(abs(e)) first
HVX_Vector vmax0_sf = hvx_vec_reduce_max_fp32(hvx_vec_abs_fp32(vx[0]));
HVX_Vector vmax1_sf = hvx_vec_reduce_max_fp32(hvx_vec_abs_fp32(vx[1]));
HVX_Vector vmax2_sf = hvx_vec_reduce_max_fp32(hvx_vec_abs_fp32(vx[2]));
HVX_Vector vmax3_sf = hvx_vec_reduce_max_fp32(hvx_vec_abs_fp32(vx[3]));
// Load and convert into QF32
HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero); // 32 elements
HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero); // 32 elements
HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero); // 32 elements
HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero); // 32 elements
// Convert to QF32
HVX_Vector vmax0_qf = Q6_Vqf32_vsub_VsfVsf(vmax0_sf, zero);
HVX_Vector vmax1_qf = Q6_Vqf32_vsub_VsfVsf(vmax1_sf, zero);
HVX_Vector vmax2_qf = Q6_Vqf32_vsub_VsfVsf(vmax2_sf, zero);
HVX_Vector vmax3_qf = Q6_Vqf32_vsub_VsfVsf(vmax3_sf, zero);
// Combine and convert to fp16
HVX_Vector vmax01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax1_qf, vmax0_qf)));
HVX_Vector vmax23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vmax3_qf, vmax2_qf)));
// Convert into fp16
HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
// Replicate first fp16 scale across all lanes
HVX_Vector ctrl = *(const HVX_Vector *) repl_2x_fp16;
vmax01_hf = Q6_V_vdelta_VV(vmax01_hf, ctrl);
vmax23_hf = Q6_V_vdelta_VV(vmax23_hf, ctrl);
HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008)); // 1.0 / 127.0
HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008)); // 1.0 / 127.0
HVX_Vector vd01_hf = Q6_Vhf_equals_Vqf16(vd01_qf16);
HVX_Vector vd23_hf = Q6_Vhf_equals_Vqf16(vd23_qf16);
hvx_vec_store_u(y_d + 0, 2, vd01_hf);
HVX_Vector rotated_vd_hf = Q6_V_vror_VR(vd01_hf, 64);
hvx_vec_store_u(y_d + 2, 2, rotated_vd_hf);
hvx_vec_store_u(y_d + 4, 2, vd23_hf);
rotated_vd_hf = Q6_V_vror_VR(vd23_hf, 64);
hvx_vec_store_u(y_d + 6, 2, rotated_vd_hf);
// Divide input by the scale
HVX_Vector vd01_inv_hf = hvx_vec_inverse_fp16(vd01_hf);
HVX_Vector vd23_inv_hf = hvx_vec_inverse_fp16(vd23_hf);
vx01_hf = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
vx23_hf = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
// Convert to int8
HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
HVX_Vector vx_i8 = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
*(HVX_Vector *) y_q = vx_i8;
}
static inline void quantize_block_fp32_q8x2(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
assert((unsigned long) x % 128 == 0);
assert((unsigned long) y_q % 128 == 0);
HVX_Vector * vx = (HVX_Vector *) x;
// Load and convert into QF32
HVX_Vector zero = Q6_V_vsplat_R(0);
HVX_Vector vx0_qf = Q6_Vqf32_vsub_VsfVsf(vx[0], zero); // 32 elements
HVX_Vector vx1_qf = Q6_Vqf32_vsub_VsfVsf(vx[1], zero); // 32 elements
HVX_Vector vx2_qf = Q6_Vqf32_vsub_VsfVsf(vx[2], zero); // 32 elements
HVX_Vector vx3_qf = Q6_Vqf32_vsub_VsfVsf(vx[3], zero); // 32 elements
// Convert into fp16
HVX_Vector vx01_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx1_qf, vx0_qf)));
HVX_Vector vx23_hf = Q6_Vh_vdeal_Vh(Q6_Vhf_equals_Wqf32(Q6_W_vcombine_VV(vx3_qf, vx2_qf)));
// Compute max and scale
HVX_Vector vmax01_hf = hvx_vec_reduce_max_fp16(hvx_vec_abs_fp16(vx01_hf));
HVX_Vector vmax23_hf = hvx_vec_reduce_max_fp16(hvx_vec_abs_fp16(vx23_hf));
// Replicate first fp16 scale across all lanes
HVX_Vector ctrl = *(const HVX_Vector *) repl_1x_fp16;
vmax01_hf = Q6_V_vdelta_VV(vmax01_hf, ctrl);
vmax23_hf = Q6_V_vdelta_VV(vmax23_hf, ctrl);
HVX_Vector vd01_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax01_hf, Q6_Vh_vsplat_R(0x2008)); // 1.0 / 127.0
HVX_Vector vd23_qf16 = Q6_Vqf16_vmpy_VhfVhf(vmax23_hf, Q6_Vh_vsplat_R(0x2008)); // 1.0 / 127.0
HVX_Vector vd01_hf = Q6_Vhf_equals_Vqf16(vd01_qf16);
HVX_Vector vd23_hf = Q6_Vhf_equals_Vqf16(vd23_qf16);
hvx_vec_store_u(y_d + 0, 4, vd01_hf);
hvx_vec_store_u(y_d + 4, 4, vd23_hf);
// Divide input by the scale
HVX_Vector vd01_inv_hf = hvx_vec_inverse_fp16(vd01_hf);
HVX_Vector vd23_inv_hf = hvx_vec_inverse_fp16(vd23_hf);
vx01_hf = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx01_hf, vd01_inv_hf));
vx23_hf = Q6_Vhf_equals_Vqf16(Q6_Vqf16_vmpy_VhfVhf(vx23_hf, vd23_inv_hf));
// Convert to int8
HVX_Vector vx01_i16 = hvx_vec_i16_from_hf_rnd_sat(vx01_hf);
HVX_Vector vx23_i16 = hvx_vec_i16_from_hf_rnd_sat(vx23_hf);
HVX_Vector vx_i8 = Q6_Vb_vpack_VhVh_sat(vx23_i16, vx01_i16);
*(HVX_Vector *) y_q = vx_i8;
}
static inline void quantize_block_fp32_q8x4(float * restrict x, uint8_t * restrict y_q, uint8_t * restrict y_d) {
assert((unsigned long) x % 128 == 0);
assert((unsigned long) y_q % 128 == 0);
@ -1655,10 +1779,24 @@ static void quantize_row_fp32_q8x4x2(float * restrict x, uint8_t * restrict y, u
uint8_t * restrict t_d = (uint8_t *) x;
for (uint32_t i = 0; i < nb; i++) {
#if FP32_QUANTIZE_GROUP_SIZE == 32
quantize_block_fp32_q8x1(x + (i * 2 + 0) * qk / 2, y_q + (i * 2 + 0) * qblk_size / 2,
t_d + (i * 2 + 0) * dblk_size / 2);
quantize_block_fp32_q8x1(x + (i * 2 + 1) * qk / 2, y_q + (i * 2 + 1) * qblk_size / 2,
t_d + (i * 2 + 1) * dblk_size / 2);
#elif FP32_QUANTIZE_GROUP_SIZE == 64
quantize_block_fp32_q8x2(x + (i * 2 + 0) * qk / 2, y_q + (i * 2 + 0) * qblk_size / 2,
t_d + (i * 2 + 0) * dblk_size / 2);
quantize_block_fp32_q8x2(x + (i * 2 + 1) * qk / 2, y_q + (i * 2 + 1) * qblk_size / 2,
t_d + (i * 2 + 1) * dblk_size / 2);
#elif FP32_QUANTIZE_GROUP_SIZE == 128
quantize_block_fp32_q8x4(x + (i * 2 + 0) * qk / 2, y_q + (i * 2 + 0) * qblk_size / 2,
t_d + (i * 2 + 0) * dblk_size / 2);
quantize_block_fp32_q8x4(x + (i * 2 + 1) * qk / 2, y_q + (i * 2 + 1) * qblk_size / 2,
t_d + (i * 2 + 1) * dblk_size / 2);
#else
#error "FP32_QUANTIZE_GROUP_SIZE must be 32, 64, or 128"
#endif
}
// now copy the scales into final location
@ -1671,6 +1809,7 @@ static void quantize_fp32_q8x4x2(const struct htp_tensor * src,
uint32_t nth,
uint32_t ith,
uint32_t nrows_per_thread) {
uint64_t t1 = HAP_perf_get_qtimer_count();
const uint32_t ne0 = src->ne[0];

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