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CPU Deployment

IREE supports efficient program execution on CPU devices by using LLVM to compile all dense computations in each program into highly optimized CPU native instruction streams, which are embedded in one of IREE's deployable formats.

To compile a program for CPU execution, pick one of IREE's supported executable formats:

Executable Format Description
embedded ELF portable, high performance dynamic library
system library platform-specific dynamic library (.so, .dll, etc.)
VMVX reference target

At runtime, CPU executables can be loaded using one of IREE's CPU HAL drivers:

  • local-task: asynchronous, multithreaded driver built on IREE's "task" system
  • local-sync: synchronous, single-threaded driver that executes work inline

Todo

Add IREE's CPU support matrix: what architectures are supported; what architectures are well optimized; etc.

Get compiler and runtime

Get compiler for CPU native instructions

Download as Python package

Python packages for various IREE functionalities are regularly published to PyPI. See the Python Bindings page for more details. The core iree-compiler package includes the LLVM-based CPU compiler:

python -m pip install iree-compiler

Tip

iree-compile is 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 compiler from source

Please make sure you have followed the Getting started page to build IREE for your host platform and the Android cross-compilation page if you are cross compiling for Android. The LLVM (CPU) compiler backend is compiled in by default on all platforms.

Ensure that the IREE_TARGET_BACKEND_LLVM_CPU CMake option is ON when configuring for the host.

Tip

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

Compile and run the model

With the compiler and runtime for local CPU execution, we can now compile a model and run it.

Compile the model

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,

Compile using the command-line

Run the following command (passing --iree-input-type= as needed for your import tool):

iree-compile \
    --iree-hal-target-backends=llvm-cpu \
    --iree-input-type=mhlo \
    iree_input.mlir -o mobilenet_cpu.vmfb

where iree_input.mlir is the imported program.

Tip

The --iree-llvm-target-triple= flag tells the compiler to generate code for a specific type of CPU. You can see the list of supported targets with iree-compile --iree-llvm-list-targets, or omit the flag to let LLVM infer the triple from your host machine (e.g. x86_64-linux-gnu).

Get IREE runtime with local CPU HAL driver

You will need to get an IREE runtime that supports the local CPU HAL driver, along with the appropriate executable loaders for your application.

Build runtime from source

Please make sure you have followed the Getting started page to build IREE for your host platform and the Android cross-compilation page if you are cross compiling for Android. The local CPU HAL drivers are compiled in by default on all platforms.

Ensure that the IREE_HAL_DRIVER_LOCAL_TASK and IREE_HAL_EXECUTABLE_LOADER_EMBEDDED_ELF (or other executable loader) CMake options are ON when configuring for the target.

Run the model

Run using the command-line

In the build directory, run the following command:

tools/iree-run-module \
    --device=local-task \
    --module_file=mobilenet_cpu.vmfb \
    --entry_function=predict \
    --function_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.