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Inactivity Notice

Note We haven't been able to invest as much time into this as we'd like lately. For a more actively supported packaged solution combining torch-mlir and IREE, see SHARK.

Torch Frontend for IREE

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

This project is under active development and is subject to frequent changes.

Example Usage

Training & Inference (functorch-based)

An end-to-end example of training a PyTorch basic regression model on IREE can be found in this script. This script uses functorch to define the model's forward and backward pass.

Inference (nn.Module-based)

An end-to-end example of compiling an nn.Module-based PyTorch BERT model to IREE can be found in this notebook. The notebook also demonstrates the significantly smaller runtime size of the compiled model when compared to PyTorch (~4MB versus ~700MB).

Native, On-device Training

A small (~100-250KB), self-contained binary can be built for deploying to resource-constrained environments. An example illustrating this can be found in this example. This binary runs a model without a Python interpreter.

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 (`llvm-cpu`).
(iree-torch.venv) $ tools/e2e_test.sh
# Run all tests on the `vmvx` backend.
(iree-torch.venv) $ tools/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/e2e_test.sh --filter Elementwise --verbose
# Shorter option names.
(iree-torch.venv) $ tools/e2e_test.sh -f Elementwise -v