GPU deployment using CUDA

IREE can accelerate model execution on Nvidia GPUs using CUDA.

Prerequisites

In order to use CUDA to drive the GPU, you need to have a functional CUDA environment. It can be verified by the following steps:

nvidia-smi | grep CUDA

If nvidia-smi does not exist, you will need to install the latest CUDA Toolkit SDK.

Get the IREE compiler

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 CUDA 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 the IREE compiler, then enable the CUDA compiler target with the IREE_TARGET_BACKEND_CUDA option.

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 includes the CUDA HAL driver.

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

$ iree-run-module --list_drivers

        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

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

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 CUDA 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 the Getting started page to build IREE from source, then enable the CUDA HAL driver with the IREE_HAL_DRIVER_CUDA option.

Compile and run a program model

With the compiler and runtime ready, we can now compile programs and run them on GPUs.

Compile a program

The IREE compiler transforms a model into its final deployable format in many 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 MobileNet v2 as an example, you can download the SavedModel with trained weights from TensorFlow Hub and convert it using IREE's TensorFlow importer. Then run one of the following commands to compile:

iree-compile \
    --iree-hal-target-backends=cuda \
    --iree-cuda-target=<...> \
    mobilenet_iree_input.mlir -o mobilenet_cuda.vmfb

Canonically a CUDA target (iree-cuda-target) matching the LLVM NVPTX backend of the form sm_<arch_number> is needed to compile towards each GPU architecture. If no architecture is specified then we will default to sm_60.

Here is a table of commonly used architectures:

CUDA GPU	Target Architecture	Architecture Code Name
NVIDIA P100	sm_60	pascal
NVIDIA V100	sm_70	volta
NVIDIA A100	sm_80	ampere
NVIDIA H100	sm_90	hopper
NVIDIA RTX20 series	sm_75	turing
NVIDIA RTX30 series	sm_86	ampere
NVIDIA RTX40 series	sm_89	ada

In addition to the canonical sm_<arch_number> scheme, iree-cuda-target also supports two additonal schemes to make a better developer experience:

• Architecture code names like volta or ampere
• GPU product names like a100 or rtx3090

These two schemes are translated into the canonical form under the hood. We add support for common code/product names without aiming to be exhaustive. If the ones you want are missing, please use the canonical form.

Run a compiled program

Run the following command:

iree-run-module \
    --device=cuda \
    --module=mobilenet_cuda.vmfb \
    --function=predict \
    --input="1x224x224x3xf32=0"

The above assumes the exported function in the model is named as predict 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.