The Torch-MLIR project aims to provide first class compiler support from the PyTorch ecosystem to the MLIR ecosystem.

This project is participating in the LLVM Incubator process: as such, it is not part of any official LLVM release. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project is not yet endorsed as a component of LLVM.

PyTorch An open source machine learning framework that accelerates the path from research prototyping to production deployment.

MLIR The MLIR project is a novel approach to building reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain specific compilers, and aid in connecting existing compilers together.

Torch-MLIR Multiple Vendors use MLIR as the middle layer, mapping from platform frameworks like PyTorch, JAX, and TensorFlow into MLIR and then progressively lowering down to their target hardware. We have seen half a dozen custom lowerings from PyTorch to MLIR. Having canonical lowerings from the PyTorch ecosystem to the MLIR ecosystem provides much needed relief to hardware vendors to focus on their unique value rather than implementing yet another PyTorch frontend for MLIR. The goal is to be similar to current hardware vendors adding LLVM target support instead of each one also implementing Clang / a C++ frontend.

We have few paths to lower down to the Torch MLIR Dialect.

• TorchScript This is the most tested path down to Torch MLIR Dialect.
• LazyTensorCore Read more details here.
• We also have basic TorchDynamo/PyTorch 2.0 support, see our long-term roadmap and Thoughts on PyTorch 2.0 for more details.

• #torch-mlir channel on the LLVM Discord - this is the most active communication channel
• Github issues here
• torch-mlir section of LLVM Discourse
• Weekly meetings on Mondays 9AM PST. See here for more information.
• Weekly op office hours on Thursdays 8:30-9:30AM PST. See here for more information.

At the time of writing, we release pre-built snapshot of torch-mlir for Python 3.10 on Linux and macOS.

If you have Python 3.10, the following commands initialize a virtual environment.

python3.10 -m venv mlir_venv
source mlir_venv/bin/activate

Or, if you want to switch over multiple versions of Python using conda, you can create a conda environment with Python 3.10.

conda create -n torch-mlir python=3.10
conda activate torch-mlir
python -m pip install --upgrade pip

Then, we can install torch-mlir with the corresponding torch and torchvision nightlies.

pip install --pre torch-mlir torchvision \
  -f https://llvm.github.io/torch-mlir/package-index/
  --extra-index-url https://download.pytorch.org/whl/nightly/cpu

Standalone script to Convert a PyTorch ResNet18 model to MLIR and run it on the CPU Backend:

# Get the latest example if you haven't checked out the code
wget https://raw.githubusercontent.com/llvm/torch-mlir/main/examples/torchscript_resnet18.py

# Run ResNet18 as a standalone script.
python examples/torchscript_resnet18.py

load image from https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg
Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /home/mlir/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
100.0%
PyTorch prediction
[('Labrador retriever', 70.66319274902344), ('golden retriever', 4.956596374511719), ('Chesapeake Bay retriever', 4.195662975311279)]
torch-mlir prediction
[('Labrador retriever', 70.66320037841797), ('golden retriever', 4.956601619720459), ('Chesapeake Bay retriever', 4.195651531219482)]

View examples here.

The project follows the conventions of typical MLIR-based projects:

• include/torch-mlir, lib structure for C++ MLIR compiler dialects/passes.
• test for holding test code.
• tools for torch-mlir-opt and such.
• python top level directory for Python code

If you would like to develop and build torch-mlir from source please look at Development Notes