The ExecuTorch Vulkan delegate is a native GPU delegate for ExecuTorch that is built on top of the cross-platform Vulkan GPU API standard. It is primarily designed to leverage the GPU to accelerate model inference on Android devices, but can be used on any platform that supports an implementation of Vulkan: laptops, servers, and edge devices.
::::{note} The Vulkan delegate is currently under active development, and its components are subject to change. ::::
Vulkan is a low-level GPU API specification developed as a successor to OpenGL. It is designed to offer developers more explicit control over GPUs compared to previous specifications in order to reduce overhead and maximize the capabilities of the modern graphics hardware.
Vulkan has been widely adopted among GPU vendors, and most modern GPUs (both desktop and mobile) in the market support Vulkan. Vulkan is also included in Android from Android 7.0 onwards.
Note that Vulkan is a GPU API, not a GPU Math Library. That is to say it provides a way to execute compute and graphics operations on a GPU, but does not come with a built-in library of performant compute kernels.
The ExecuTorch Vulkan Delegate is a wrapper around a standalone runtime known as the Vulkan Compute Library. The aim of the Vulkan Compute Library is to provide GPU implementations for PyTorch operators via GLSL compute shaders.
The Vulkan Compute Library is a fork/iteration of the PyTorch Vulkan Backend. The core components of the PyTorch Vulkan backend were forked into ExecuTorch and adapted for an AOT graph-mode style of model inference (as opposed to PyTorch which adopted an eager execution style of model inference).
The components of the Vulkan Compute Library are contained in the executorch/backends/vulkan/runtime/ directory. The core components are listed and described below:
runtime/
├── api/ .................... Wrapper API around Vulkan to manage Vulkan objects
└── graph/ .................. ComputeGraph class which implements graph mode inference
└── ops/ ................ Base directory for operator implementations
├── glsl/ ........... GLSL compute shaders
│ ├── *.glsl
│ └── conv2d.glsl
└── impl/ ........... C++ code to dispatch GPU compute shaders
├── *.cpp
└── Conv2d.cpp
The Vulkan delegate currently supports the following features:
In addition to increasing operator coverage, the following features are currently in development:
To further understand the features of the Vulkan Delegate and how to use it, consider the following end to end example with a simple single operator model.
Assuming ExecuTorch has been set up and installed, the following script can be used to produce a lowered MobileNet V2 model as vulkan_mobilenetv2.pte.
Once ExecuTorch has been set up and installed, the following script can be used to generate a simple model and lower it to the Vulkan delegate.
# Note: this script is the same as the script from the "Setting up ExecuTorch"
# page, with one minor addition to lower to the Vulkan backend.
import torch
from torch.export import export
from executorch.exir import to_edge
from executorch.backends.vulkan.partitioner.vulkan_partitioner import VulkanPartitioner
# Start with a PyTorch model that adds two input tensors (matrices)
class Add(torch.nn.Module):
def __init__(self):
super(Add, self).__init__()
def forward(self, x: torch.Tensor, y: torch.Tensor):
return x + y
# 1. torch.export: Defines the program with the ATen operator set.
aten_dialect = export(Add(), (torch.ones(1), torch.ones(1)))
# 2. to_edge: Make optimizations for Edge devices
edge_program = to_edge(aten_dialect)
# 2.1 Lower to the Vulkan backend
edge_program = edge_program.to_backend(VulkanPartitioner())
# 3. to_executorch: Convert the graph to an ExecuTorch program
executorch_program = edge_program.to_executorch()
# 4. Save the compiled .pte program
with open("vk_add.pte", "wb") as file:
file.write(executorch_program.buffer)
Like other ExecuTorch delegates, a model can be lowered to the Vulkan Delegate using the to_backend() API. The Vulkan Delegate implements the VulkanPartitioner class which identifies nodes (i.e. operators) in the graph that are supported by the Vulkan delegate, and separates compatible sections of the model to be executed on the GPU.
This means the a model can be lowered to the Vulkan delegate even if it contains some unsupported operators. This will just mean that only parts of the graph will be executed on the GPU.
::::{note} The supported ops list Vulkan partitioner code can be inspected to examine which ops are currently implemented in the Vulkan delegate. ::::
The easiest way to build and test the Vulkan Delegate is to build for Android and test on a local Android device. Android devices have built in support for Vulkan, and the Android NDK ships with a GLSL compiler which is needed to compile the Vulkan Compute Library's GLSL compute shaders.
The Vulkan Delegate libraries can be built by setting -DEXECUTORCH_BUILD_VULKAN=ON when building with CMake.
First, make sure that you have the Android NDK installed; any NDK version past NDK r19c should work. Note that the examples in this doc have been validated with NDK r25. The Android SDK should also be installed so that you have access to adb.
The instructions in this page assumes that the following environment variables are set.
export ANDROID_NDK=<path_to_ndk> # Select the appropriate Android ABI for your device export ANDROID_ABI=arm64-v8a # All subsequent commands should be performed from ExecuTorch repo root cd <path_to_executorch_root> # Make sure adb works adb --version
To build and install ExecuTorch libraries (for Android) with the Vulkan Delegate:
# From executorch root directory (rm -rf cmake-android-out && \ pp cmake . -DCMAKE_INSTALL_PREFIX=cmake-android-out \ -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ -DANDROID_ABI=$ANDROID_ABI \ -DEXECUTORCH_BUILD_VULKAN=ON \ -DPYTHON_EXECUTABLE=python \ -Bcmake-android-out && \ cmake --build cmake-android-out -j16 --target install)
::::{note} Since operator support is currently limited, only binary arithmetic operators will run on the GPU. Expect inference to be slow as the majority of operators are being executed via Portable operators. ::::
Now, the partially delegated model can be executed (partially) on your device's GPU!
# Build a model runner binary linked with the Vulkan delegate libs cmake --build cmake-android-out --target vulkan_executor_runner -j32 # Push model to device adb push vk_add.pte /data/local/tmp/vk_add.pte # Push binary to device adb push cmake-android-out/backends/vulkan/vulkan_executor_runner /data/local/tmp/runner_bin # Run the model adb shell /data/local/tmp/runner_bin --model_path /data/local/tmp/vk_add.pte