Torch frontend for IREE [Under Construction]

This project provides end-to-end flows supporting users of PyTorch that want to target IREE as a compiler backend. We use the Torch-MLIR project to provide our PyTorch frontend.

Planned features

• Python (or, if absolutely necessary, C++) code that pulls in the bindings from both projects into an end-to-end flow for users.
• Docker images for users to be able to quickly get started
• CI of the Torch-MLIR end-to-end tests, with IREE plugged in as a backend
• User examples:
  • Jupyter notebooks using the above to demonstrate interactive use of the tools
  • Standalone user-level Python code demonstrating various deployment flows (mobile, embedded).

Running end-to-end correctness tests

Setup the venv for running:

# Create a Python virtual environment.
$ python -m venv iree-torch.venv
$ source iree-torch.venv/bin/activate

# Option 1: Install Torch-MLIR and IREE from nightly packages:
(iree-torch.venv) $ python -m pip install -r "${IREE_TORCH_SRC_ROOT}/requirements.txt"

# Option 2: For development, build from source and set `PYTHONPATH`:
ninja -C "${TORCH_MLIR_BUILD_ROOT}" TorchMLIRPythonModules
ninja -C "${IREE_BUILD_ROOT}" IREECompilerPythonModules bindings_python_iree_runtime_runtime
export PYTHONPATH="${IREE_BUILD_ROOT}/runtime/bindings/python:${IREE_BUILD_ROOT}/compiler/bindings/python:${TORCH_MLIR_BUILD_ROOT}/tools/torch-mlir/python_packages/torch_mlir:${PYTHONPATH}"

Run the Torch-MLIR TorchScript e2e test suite on IREE:

# Run all the tests on the default backend (`dylib`).
(iree-torch.venv) $ tools/torchscript_e2e_test.sh
# Run all tests on the `vmvx` backend.
(iree-torch.venv) $ tools/torchscript_e2e_test.sh --config vmvx
# Filter the tests (with a regex) and report failures with verbose error messages.
# This is good for drilling down on a single test as well.
(iree-torch.venv) $ tools/torchscript_e2e_test.sh --filter Elementwise --verbose
# Shorter option names.
(iree-torch.venv) $ tools/torchscript_e2e_test.sh -f Elementwise -v