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Author: Arm Limited

Requires Python: >=3.10

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ML Emulation Layer for Vulkan®

Arm® has approached the Khronos® group with a set of Machine Learning extensions for the Vulkan® and SPIR-V™ APIs. On devices where these extensions have not been implemented by the Vulkan® Installable Device Drivers (ICD), the ML Emulation Layer is required.

The ML Emulation Layer for Vulkan® provides an implementation of the ML APIs enabling ML workloads to be executed on any Vulkan® Compute capable device. The ML Emulation Layer for Vulkan® is split into separate graph and tensor layers that are inserted by the Vulkan® Loader.

The graph layer exposes:

VK_ARM_data_graph
VK_ARM_data_graph_instruction_set_tosa
VK_ARM_data_graph_optical_flow

The tensor layer exposes:

VK_ARM_tensors

The corresponding SPIR-V™ extensions and extended instruction sets currently used by the ML Emulation Layer for Vulkan® are:

SPV_ARM_graph
SPV_ARM_tensors
TOSA.001000.1
Arm.MotionEngine.100

Cloning the repository

To clone the ML Emulation Layer for Vulkan® as a stand-alone repository, you can use regular git clone commands. However, for better management of dependencies and to ensure everything is placed in the appropriate directories, we recommend using the git-repo tool to clone the repository as part of the ML SDK for Vulkan® suite. Repo tool.

For a minimal build and to initialize only the ML Emulation Layer for Vulkan® and its dependencies, run:

repo init -u https://github.com/arm/ai-ml-sdk-manifest -g emulation-layer

Alternatively, to initialize the repo structure for the entire ML SDK for Vulkan®, including the ML Emulation Layer for Vulkan®, run:

repo init -u https://github.com/arm/ai-ml-sdk-manifest -g all

After the repo is initialized, you can fetch the contents with:

repo sync --no-clone-bundle

Cloning on Windows®

To ensure nested submodules do not exceed the maximum long path length, you must enable long paths on Windows®, and you must clone close to the root directory or use a symlink. Make sure to use Git for Windows.

Using PowerShell:

Set-ItemProperty -Path "HKLM:\SYSTEM\CurrentControlSet\Control\FileSystem" -Name "LongPathsEnabled" -Value 1
git config --global core.longpaths true
git --version # Ensure you are using Git for Windows, for example 2.50.1.windows.1
git clone <git-repo-tool-url>
python <path-to-git-repo>\git-repo\repo init -u <manifest-url> -g all
    python <path-to-git-repo>\git-repo\repo sync --no-clone-bundle

Using Git Bash:

cmd.exe "/c reg.exe add \"HKLM\System\CurrentControlSet\Control\FileSystem"" /v LongPathsEnabled /t REG_DWORD /d 1 /f"
git config --global core.longpaths true
git --version # Ensure you are using the Git for Windows, for example 2.50.1.windows.1
git clone <git-repo-tool-url>
python <path-to-git-repo>/git-repo/repo init -u <manifest-url> -g all
python <path-to-git-repo>/git-repo/repo sync --no-clone-bundle

After the sync command completes successfully, you can find the ML SDK Emulation Layer for Vulkan® in <repo_root>/sw/emulation-layer/. You can also find all the dependencies required by the ML Emulation Layer for Vulkan® in <repo_root>/dependencies/.

Building the ML Emulation Layer for Vulkan® from source

The build system must have:

C/C++ 17 compiler: GCC or Clang on Linux, Clang on Darwin, or MSVC on Windows®.
CMake 3.25 or later.
Ninja 1.8.2 or later.
Python 3.10 or later. Required python libraries for building are listed in tooling-requirements.txt.
Vulkan® SDK 1.4.328.1 or later.

The following dependencies are also needed:

For the preferred dependency versions see the manifest file.

Building with the script

To make the build configuration options easily discoverable, we provide a python build script. When you run the script from a git-repo manifest checkout, the script uses default paths and does not require any additional arguments. If you do not use the script, you must specify paths to the dependencies.

To build on the current platform, for example on Linux or Windows®, run the following command:

python3 $SDK_PATH/sw/emulation-layer/scripts/build.py -j $(nproc)

To cross compile for AArch64 architecture, add the following option:

python3 $SDK_PATH/sw/emulation-layer/scripts/build.py -j $(nproc) --target-platform aarch64

To enable and run tests, use the --test option. To lint the tests, use the --lint option. To build the documentation, use the --doc option. To build the documentation, you must have sphinx and doxygen installed on your machine.

You can install the build artifacts for this project into a specified location. To install the build artifacts, pass the --install option with the required path.

To create an archive with the build artifacts option, you must add the --package option. The archive is stored in the provided location.

For more command line options, see the help output:

python3 $SDK_PATH/sw/emulation-layer/scripts/build.py --help

Usage

The ML Emulation Layer for Vulkan® is loaded as two explicit Vulkan® layers. The platform-specific sections below show the exact commands for each operating system, but the same setup sequence applies on all platforms:

Make the Vulkan® loader discover the layer manifest files.
Make the platform dynamic loader discover the graph and tensor layer libraries.
Enable the graph layer before the tensor layer, either during Vulkan® instance creation or with VK_INSTANCE_LAYERS.
Configure any optional logging or profiling environment variables before starting the application.

The layer names are:

VK_LAYER_ML_Graph_Emulation
VK_LAYER_ML_Tensor_Emulation

The manifest files are:

VkLayer_Graph.json
VkLayer_Tensor.json

For more information about using explicit Vulkan® layers, see the Vulkan® Layer Documentation.

Logging

You can enable logging using environment variables. Logging must be set before the application is started. Logging severity can be one of error, warning, info, or debug. Logging severity is set independently for the graph and tensor layer.

Using shell:

export VMEL_GRAPH_SEVERITY=debug
export VMEL_TENSOR_SEVERITY=info

Using PowerShell:

$env:VMEL_GRAPH_SEVERITY="debug"
$env:VMEL_TENSOR_SEVERITY="info"

Common severity for both layers can be set using the following variable:

export VMEL_COMMON_SEVERITY=debug

$env:VMEL_COMMON_SEVERITY="debug"

Graph Profiling

You can enable per-pipeline graph profiling with Vulkan® timestamp queries using environment variables before starting the application. Profiling covers TOSA graph operators, MotionEngine graph operators, and optical-flow compute pipelines. Profiling is disabled by default. When enabled, graph command-buffer submits remain asynchronous. Timestamp results are collected when the application waits on fences, waits for a queue or device to become idle, or when the profiling property is queried. Profiling results are saved only as a queryable data graph pipeline property.

Using shell:

export VMEL_GRAPH_PROFILING=1

Using PowerShell:

$env:VMEL_GRAPH_PROFILING="1"

The profiling property returns JSON with a samples array containing one entry per profiled internal compute dispatch, including pipeline_kind, operator_name, raw cycle counts, and time_ms, plus a by_operator summary with total, average, minimum, and maximum time per profiled pipeline.

Graph profiling requires a queue family with non-zero timestampValidBits. When the selected Vulkan® driver does not expose timestamp queries, graph execution remains available but no timestamp samples can be collected.

Usage on Linux

You can enable the graph and tensor layers using environment variables only, without modifying the Vulkan® application. The following environment variables are used:

Use the LD_LIBRARY_PATH environment variable to point at the VkLayer_Graph and VkLayer_Tensor libraries.
Use the VK_ADD_LAYER_PATH environment variable to point at the VkLayer_Graph.json and VkLayer_Tensor.json manifest file.
- If your loader ignores VK_ADD_LAYER_PATH (older SDKs before 1.4.328.1), use VK_LAYER_PATH.
You must enable the graph layer before the tensor layer. To do this, use the VK_INSTANCE_LAYERS environment variable.

If you have installed the ML Emulation Layer for Vulkan® into a deploy folder, use the following environment variables to enable the layers:

export LD_LIBRARY_PATH=$PWD/deploy/lib64:$PWD/deploy/lib:$LD_LIBRARY_PATH
export VK_ADD_LAYER_PATH=$PWD/deploy/share/vulkan/explicit_layer.d
export VK_INSTANCE_LAYERS=VK_LAYER_ML_Graph_Emulation:VK_LAYER_ML_Tensor_Emulation

Usage on Windows®

You can enable the graph and tensor layers using environment variables only, without modifying the Vulkan® application. The following environment variables are used:

Use the VK_ADD_LAYER_PATH environment variable to point at the VkLayer_Graph.json and VkLayer_Tensor.json manifest files.
You must enable the graph layer before the tensor layer. To do this, use the VK_INSTANCE_LAYERS environment variable.

If you have installed the ML Emulation Layer for Vulkan® into a deploy folder, use the following environment variables to enable the layers:

$env:VK_LAYER_PATH="$PWD\deploy\bin"
$env:VK_INSTANCE_LAYERS="VK_LAYER_ML_Graph_Emulation;VK_LAYER_ML_Tensor_Emulation"

Alternatively, you can use the Windows® registry keys to load the manifest files. This can be done using the Windows® GUI. Or, if you have installed the ML Emulation Layer for Vulkan® into a deploy folder, you set the path to the manifest files using:

reg add HKEY_LOCAL_MACHINE\SOFTWARE\Khronos\Vulkan\ExplicitLayers /v `
{ABSOLUTE_PATH}\deploy\bin /t REG_DWORD /d 0 /f

$env:VK_INSTANCE_LAYERS="VK_LAYER_ML_Graph_Emulation;VK_LAYER_ML_Tensor_Emulation"

If running a Windows® terminal with elevated permissions, `VK_ADD_LAYER_PATH` is ignored
for security reasons. However, if `VK_ADD_LAYER_PATH` is set and not ignored, then Vulkan®
skips searching the registry keys for manifest files.

Building for Android™ (Experimental)

The Android™ NDK toolset is required to build the ML Emulation Layer for Vulkan® for an Android™ device. The Android™ device must have Vulkan® API 1.3 support.

To build the ML Emulation Layer for Vulkan®, run:

cmake -B build
   -DCMAKE_TOOLCHAIN_FILE=${NDK}/build/cmake/android.toolchain.cmake \
   -DANDROID_ABI=arm64-v8a                                           \
   -DGLSLANG_PATH=${REPO}/dependencies/glslang                       \
   -DSPIRV_CROSS_PATH=${REPO}/dependencies/SPIRV-Cross               \
   -DSPIRV_HEADERS_PATH=${REPO}/dependencies/SPIRV-Headers           \
   -DSPIRV_TOOLS_PATH=${REPO}/dependencies/SPIRV-Tools               \
   -DVULKAN_HEADERS_PATH=${REPO}/dependencies/Vulkan-Headers

cmake --build build

Usage on Android™ (Experimental)

You can pack the graph and tensor layer libraries into the Application Package Kit (APK) or push to the /data/local/debug/vulkan directory for Android™ to discover the ML Emulation Layer for Vulkan®. Applications can enable the layers during Vulkan® instance creation or you can enable the layers without modifying the application by using following commands:

adb shell settings put global enable_gpu_debug_layers 1
adb shell settings put global gpu_debug_app $TARGET_APP_PKG
adb shell settings put global gpu_debug_layers \
    VK_LAYER_ML_Graph_Emulation:VK_LAYER_ML_Tensor_Emulation

APK Packaging

If you want to package the ML Emulation Layer for Vulkan® as an Android™ APK, set the following variables first:

export EMULATION_LAYER_ROOT=/path/to/emulation-layer
export NDK=/path/to/android-ndk
export ANDROID_HOME=/path/to/android-sdk
export TARGET_APP_PKG=com.example.targetapp

The Android™ packaging flow in scripts/build.py requires the Android™ NDK toolchain for the native build and Gradle 8.4 or later with ANDROID_HOME set for APK generation. The Android™ SDK installation pointed to by ANDROID_HOME should include build-tools;34.0.0 and platforms;android-34, or other compatible versions. A typical APK packaging command looks like:

python3 $EMULATION_LAYER_ROOT/scripts/build.py \
    --build-type Android \
    --target-platform android \
    --cmake-toolchain-for-android $NDK/build/cmake/android.toolchain.cmake \
    --install $EMULATION_LAYER_ROOT/apk_install \
    --package-type apk \
    -j $(nproc)

This produces an Android™ project in apk_package/ and Gradle builds the debug APK from there. The layer APK package name is currently com.arm.ai_ml_emulation_layer_for_vulkan.

To enable the packaged layers for a target application, use Android™ GPU debug layer settings:

adb shell settings put global enable_gpu_debug_layers 1
adb shell settings put global gpu_debug_app $TARGET_APP_PKG
adb shell settings put global gpu_debug_layers \
    VK_LAYER_ML_Graph_Emulation:VK_LAYER_ML_Tensor_Emulation
adb shell settings put global gpu_debug_layer_app \
    com.arm.ai_ml_emulation_layer_for_vulkan

If you only want to enable a single layer, the command will likely look like:

adb shell settings put global gpu_debug_layers VK_LAYER_KHRONOS_validation

The layer package must also be visible to the debug app. On Android™ 11 and later, if the target app does not already query the layer package, add a package visibility entry such as:

<queries>
    <package android:name="com.arm.ai_ml_emulation_layer_for_vulkan" />
</queries>

Refer to the Android™ validation layer guide for background on APK packaging, debug layer settings, and package visibility: Use Vulkan® validation layers on Android™.

Building for Darwin (Experimental)

Install the LunarG Vulkan® SDK to obtain the Vulkan® Loader. Recent SDK releases can also install KosmicKrisp as an opt-in technical preview; check that SDK release's host requirements before selecting it.

To build the ML Emulation Layer for Vulkan®, run:

python3 "$SDK_PATH/sw/emulation-layer/scripts/build.py" \
    --install "$SDK_PATH/deploy"

For Vulkan® SDK installation and driver requirements, see the LunarG getting-started guide.

Cross compilation for AArch64 on x86-64 (Experimental)

Shader pre-compilation is required and needs a glslang compiler that runs on the build host. There are two ways to provide it when cross-compiling:

Provide a custom glslang executable. You can direct CMake to a custom glslang executable file using the GLSLANG_EXECUTABLE option. First, build glslang inside its repo. When the repository is initialized using the repo manifest, the glslang source is checked out in <repo_root>/dependencies/glslang/ For building glslang, see Building (CMake).
Install glslang to the system. Under cross compilation, when no custom glslang executable is provided, it will be searched from the system using CMake's find_program. On Ubuntu, you can install it with sudo apt install glslang-tools or from the source code following the previously mentioned documentation. Note that we require version > 15.4.0, which may not yet be available in Ubuntu’s official package repositories.

An example build flow using the option 1 would be:

First, build the glslang standalone under <repo_root>/dependencies/glslang/:

cmake -B build -S . -DCMAKE_BUILD_TYPE=Release -DENABLE_GLSLANG_BINARIES=ON -DENABLE_OPT=OFF -DBUILD_SHARED_LIBS=OFF

cmake --build build --target glslang-standalone

After building, the binary will be at <repo_root>/dependencies/glslang/build/StandAlone/glslang. Then run the following under <repo_root>/sw/emulation-layer/:

cmake -B build \
   -DCMAKE_TOOLCHAIN_FILE=${REPO}/sw/emulation-layer/cmake/toolchain/linux-aarch64-gcc.cmake \
   -DGLSLANG_PATH=${REPO}/dependencies/glslang \
   -DSPIRV_CROSS_PATH=${REPO}/dependencies/SPIRV-Cross \
   -DSPIRV_HEADERS_PATH=${REPO}/dependencies/SPIRV-Headers \
   -DSPIRV_TOOLS_PATH=${REPO}/dependencies/SPIRV-Tools \
   -DVULKAN_HEADERS_PATH=${REPO}/dependencies/Vulkan-Headers \
   -DGLSLANG_EXECUTABLE=${REPO}/dependencies/glslang/build/StandAlone/glslang

cmake --build build

Troubleshooting

All zero output from AMD GPUs on Linux

Some workloads may cause silent GPU crashes due to timeout errors. You can check for related kernel messages with the following command:

dmesg | grep -i amdgpu

To change the timeout, follow these steps (applies if your system uses GRUB as the bootloader):

Edit the GRUB configuration file:
```
sudo nano /etc/default/grub
```
Add or modify the GRUB_CMDLINE_LINUX line to include a longer timeout value in milliseconds:
```
GRUB_CMDLINE_LINUX="quiet splash amdgpu.lockup_timeout=20000"
```
Update the GRUB configuration:
```
sudo update-grub
```
Reboot the system:
```
sudo reboot
```

PyPI

The ML Emulation Layer for Vulkan® is available on PyPI as the ai-ml-emulation-layer-for-vulkan package.

Install the published package:

pip install ai-ml-emulation-layer-for-vulkan

To build and install the host layers from an ML SDK checkout, run from this repository root:

pip install .

Known Limitations

Resources created with VK_IMAGE_TILING_OPTIMAL and VK_TENSOR_TILING_OPTIMAL_ARM flags cannot be used with memory aliasing.
Data graph pipeline creation without a shader module is not supported.
Accuracy of MATMUL computation might be incorrect for mixed reduce float types such as fp8e5m2xfp8e4m3 and fp8e4m3xfp8e5m2.
Usage of the 'shaderFloat64' feature requires support from the underlying ICD. This relates to high-precision types. Support of it can be checked with:
```
vulkaninfo 2>&1 | grep -e 'shaderFloat64\|deviceName'
```
If this feature is not available, add the --use-float-as-double flag to the build script command to use 32-bit float instead of double. This behavior is automatically enabled on Darwin and Android™.

Darwin driver limitations

Optical-flow workloads are not currently supported on Darwin. MoltenVK and KosmicKrisp also expose different optional Vulkan® features, so a workload can be supported by one driver and rejected by the other. Check vulkaninfo and the selected driver's release notes when a required extension is unavailable.

MoltenVK does not have full Vulkan® coverage. Some notable issues are:

Several Vulkan® extensions are not available in MoltenVK, e.g. custom border color.
High-precision types in buffers/push constants is currently not supported, which forces lower precision to be used instead.
Passing Shader Storage Buffer Objects, SSBOs, to functions is currently not supported in MoltenVK.

KosmicKrisp is a technical preview with stricter requirements. Timestamp-based graph profiling is unavailable when its selected queue reports timestampValidBits as zero.