GPU deployment using Vulkan

IREE can accelerate model execution on GPUs via Vulkan, a low-overhead graphics and compute API. Vulkan is cross-platform: it is available on many operating systems, including Android, Linux, and Windows. Vulkan is also cross-vendor: it is supported by most GPU vendors, including AMD, ARM, Intel, NVIDIA, and Qualcomm.

Support matrix

As IREE and the compiler ecosystem it operates within matures, more target specific optimizations will be implemented. At this stage, expect reasonable performance across all GPUs and for improvements to be made over time for specific vendors and architectures.

GPU Vendor	Category	Performance	Focus Architecture
ARM Mali GPU	Mobile	Good	Valhall+
Qualcomm Adreno GPU	Mobile	Reasonable	640+
AMD GPU	Desktop/server	Good	RDNA+
NVIDIA GPU	Desktop/server	Reasonable	Turing+

Prerequisites

In order to use Vulkan to drive the GPU, you need to have a functional Vulkan environment. IREE requires Vulkan 1.1 on Android and 1.2 elsewhere. It can be verified by the following steps:

Android Linux Windows

Android mandates Vulkan 1.1 support since Android 10. You just need to make sure the device's Android version is 10 or higher.

Run the following command in a shell:

vulkaninfo | grep apiVersion

If vulkaninfo does not exist, you will need to install the latest Vulkan SDK. Installing via LunarG's package repository is recommended, as it places Vulkan libraries and tools under system paths so it's easy to discover.

If the listed version is lower than Vulkan 1.2, you will need to update the driver for your GPU.

Run the following command in a shell:

vulkaninfo | grep apiVersion

If vulkaninfo does not exist, you will need to install the latest Vulkan SDK.

If the listed version is lower than Vulkan 1.2, you will need to update the driver for your GPU.

Get the IREE compiler

Vulkan expects the program running on GPU to be expressed by the SPIR-V binary exchange format, which the model must be compiled into.

Download the compiler from a release

Python packages are distributed through multiple channels. See the Python Bindings page for more details. The core iree-base-compiler package includes the SPIR-V compiler:

Stable releases Nightly releases Development packages

Stable release packages are published to PyPI.

python -m pip install iree-base-compiler

Nightly pre-releases are published on GitHub releases.

python -m pip install \
  --find-links https://iree.dev/pip-release-links.html \
  --upgrade --pre iree-base-compiler

Development packages are built at every commit and on pull requests, for limited configurations.

On Linux with Python 3.11, development packages can be installed into a Python venv using the build_tools/pkgci/setup_venv.py script:

# Install packages from a specific commit ref.
# See also the `--fetch-latest-main` and `--fetch-gh-workflow` options.
python ./build_tools/pkgci/setup_venv.py /tmp/.venv --fetch-git-ref=8230f41d
source /tmp/.venv/bin/activate

Tip

iree-compile and other tools are installed to your python module installation path. If you pip install with the user mode, it is under ${HOME}/.local/bin, or %APPDATA%\Python on Windows. You may want to include the path in your system's PATH environment variable:

export PATH=${HOME}/.local/bin:${PATH}

Build the compiler from source

Please make sure you have followed the Getting started page to build IREE for your host platform. The SPIR-V compiler backend is compiled in by default on all platforms, though you should ensure that the IREE_TARGET_BACKEND_VULKAN_SPIRV CMake option is ON when configuring.

Tip

iree-compile will be built under the iree-build/tools/ directory. You may want to include this path in your system's PATH environment variable.

Get the IREE runtime

Next you will need to get an IREE runtime that supports the Vulkan HAL driver.

You can check for Vulkan support by looking for a matching driver and device:

$ iree-run-module --list_drivers

# ============================================================================
# Available HAL drivers
# ============================================================================
# Use --list_devices={driver name} to enumerate available devices.

        cuda: NVIDIA CUDA HAL driver (via dylib)
         hip: HIP HAL driver (via dylib)
  local-sync: Local execution using a lightweight inline synchronous queue
  local-task: Local execution using the IREE multithreading task system
      vulkan: Vulkan 1.x (dynamic)

$ iree-run-module --list_devices

  hip://GPU-00000000-1111-2222-3333-444444444444
  local-sync://
  local-task://
  vulkan://00000000-1111-2222-3333-444444444444

Download the runtime from a release

Python packages are distributed through multiple channels. See the Python Bindings page for more details. The core iree-base-runtime package includes the Vulkan HAL driver:

Stable releases Nightly releases Development packages

Stable release packages are published to PyPI.

python -m pip install iree-base-runtime

Nightly pre-releases are published on GitHub releases.

python -m pip install \
  --find-links https://iree.dev/pip-release-links.html \
  --upgrade --pre iree-base-runtime

Development packages are built at every commit and on pull requests, for limited configurations.

On Linux with Python 3.11, development packages can be installed into a Python venv using the build_tools/pkgci/setup_venv.py script:

# Install packages from a specific commit ref.
# See also the `--fetch-latest-main` and `--fetch-gh-workflow` options.
python ./build_tools/pkgci/setup_venv.py /tmp/.venv --fetch-git-ref=8230f41d
source /tmp/.venv/bin/activate

Build the runtime from source

Please make sure you have followed one of the Building from source pages to build IREE for your target platform. The Vulkan HAL driver is compiled in by default on supported platforms, though you should ensure that the IREE_HAL_DRIVER_VULKAN CMake option is ON when configuring.

Compile and run a program

With the requirements out of the way, we can now compile a model and run it.

Compile a program

The IREE compiler transforms a model into its final deployable format in several sequential steps. A model authored with Python in an ML framework should use the corresponding framework's import tool to convert into a format (i.e., MLIR) expected by the IREE compiler first.

Using a MobileNet model as an example, import using IREE's ONNX importer:

# Download the model you want to compile and run.
wget https://github.com/onnx/models/raw/refs/heads/main/validated/vision/classification/mobilenet/model/mobilenetv2-10.onnx

# Import to MLIR using IREE's ONNX importer.
pip install iree-base-compiler[onnx]
iree-import-onnx mobilenetv2-10.onnx --opset-version 17 -o mobilenetv2.mlir

Then run the following command to compile with the vulkan-spirv target:

iree-compile \
    --iree-hal-target-backends=vulkan-spirv \
    --iree-vulkan-target=<...> \
    mobilenetv2.mlir -o mobilenet_vulkan.vmfb

Tip - Vulkan targets

The --iree-vulkan-target specifies the GPU architecture to target. It accepts a few schemes:

• LLVM CodeGen backend style: this is using LLVM AMDGPU/NVPTX CodeGen targets like gfx1100 for AMD RX 7900XTX and sm_86 for NVIDIA RTX 3090 GPUs.
• Architecture code name style like rdna3/valhall4/ampere/adreno for AMD/ARM/NVIDIA/Qualcomm GPUs.
• Product name style: e.g., using rx7900xtx/a100 for corresponding GPUs.

Here are a few examples showing how you can target various recent common GPUs:

GPU	Target Architecture	Architecture Code Name	Product Name
AMD RX7900XTX	gfx1100	rdna3	rx7900xtx
AMD RX7900XT	gfx1100	rdna3	rx7900xt
AMD RX7800XT	gfx1101	rdna3	rx7800xt
AMD RX7700XT	gfx1101	rdna3	rx7700xt
AMD RX6000 series		rdna2
AMD RX5000 series		rdna1
ARM Mali G715		valhall4	e.g., mali-g715
ARM Mali G510		valhall3	e.g., mali-g510
ARM GPUs		valhall
NVIDIA RTX40 series	sm_89	ada	e.g., rtx4090
NVIDIA RTX30 series	sm_86	ampere	e.g., rtx3080ti
NVIDIA RTX20 series	sm_75	turing	e.g., rtx2070super
Qualcomm GPUs		adreno

If no target is specified, then a safe but more limited default will be used.

Note that we don't support the full spectrum of GPUs here and it is impossible to capture all details of a Vulkan implementation with a target triple, given the allowed variances on extensions, properties, limits, etc. So the target triple is just an approximation for usage. This is more of a mechanism to help us develop IREE itself--in the long term we want to perform multiple targetting to generate to multiple architectures if no target is given.

Run a compiled program

To run the compiled program:

iree-run-module \
    --device=vulkan \
    --module=mobilenet_vulkan.vmfb \
    --function=torch-jit-export \
    --input="1x3x224x224xf32=0"

The above assumes the exported function in the model is named torch-jit-export and it expects one 224x224 RGB image. We are feeding in an image with all 0 values here for brevity, see iree-run-module --help for the format to specify concrete values.