GPU deployment using CUDA

IREE can accelerate model execution on Nvidia GPUs using CUDA.

:octicons-download-16: 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

:octicons-download-16: 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:

--8<-- “docs/website/docs/guides/deployment-configurations/snippets/_iree-compiler-from-release.md”

:material-hammer-wrench: 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.

:octicons-download-16: 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:

--8<-- “docs/website/docs/guides/deployment-configurations/snippets/_iree-runtime-from-release.md”

:material-hammer-wrench: 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.

:octicons-checklist-24: Check for CUDA devices

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

--8<-- "docs/website/docs/guides/deployment-configurations/snippets/_iree-run-module-driver-list.md:2"

--8<-- "docs/website/docs/guides/deployment-configurations/snippets/_iree-run-module-device-list-nvidia.md"

Compile and run a program

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

:octicons-file-code-16: Compile a program

--8<-- “docs/website/docs/guides/deployment-configurations/snippets/_iree-import-onnx-mobilenet.md”

Then run the following command to compile with the cuda target:

iree-compile \
    --iree-hal-target-device=cuda \
    --iree-cuda-target=<...> \
    mobilenetv2.mlir -o mobilenet_cuda.vmfb

--8<-- “docs/website/docs/guides/deployment-configurations/snippets/_iree-optimization-options.md”

Choosing CUDA targets

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.

:octicons-terminal-16: Run a compiled program

To run the compiled program:

iree-run-module \
    --device=cuda \
    --module=mobilenet_cuda.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.