Package: torch-mlir
The Torch-MLIR ProjectThe Torch-MLIR project aims to provide first class compiler support from thePyTorchecosystem 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.PyTorchAn open source machine learning framework that accelerates the path from research prototyping to production deployment.MLIRThe 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-MLIRMultiple 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 would provide 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.All the roads from PyTorch to Torch MLIR DialectWe have few paths to lower down to the Torch MLIR Dialect.TorchScript
This is the most tested path down to Torch MLIR Dialect, and the PyTorch ecosystem is converging on using TorchScript IR as a lingua franca.LazyTensorCore
Read more detailshere.Project Communication#torch-mlirchannel on the LLVMDiscord- this is the most active communication channelGithub issuesheretorch-mlirsectionof LLVM DiscourseWeekly meetings on Mondays 9AM PST. Seeherefor more information.Weekly op office hours on Thursdays 8:30-9:30AM PST. Seeherefor more information.Install torch-mlir snapshotThis installs a pre-built snapshot of torch-mlir for Python 3.7/3.8/3.9/3.10 on Linux and macOS.python-mvenvmlir_venvsourcemlir_venv/bin/activate# Some older pip installs may not be able to handle the recent PyTorch depspython-mpipinstall--upgradepip
pipinstall--pretorch-mlirtorchvision-fhttps://llvm.github.io/torch-mlir/package-index/--extra-index-urlhttps://download.pytorch.org/whl/nightly/cpu# This will install the corresponding torch and torchvision nightliesDemosTorchScript ResNet18Standalone 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 codewgethttps://raw.githubusercontent.com/llvm/torch-mlir/main/examples/torchscript_resnet18.py# Run ResNet18 as a standalone script.pythonexamples/torchscript_resnet18.py

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PyTorchprediction[('Labrador retriever',70.66319274902344),('golden retriever',4.956596374511719),('Chesapeake Bay retriever',4.195662975311279)]torch-mlirprediction[('Labrador retriever',70.66320037841797),('golden retriever',4.956601619720459),('Chesapeake Bay retriever',4.195651531219482)]Lazy Tensor CoreView exampleshere.Repository LayoutThe project follows the conventions of typical MLIR-based projects:include/torch-mlir,libstructure for C++ MLIR compiler dialects/passes.testfor holding test code.toolsfortorch-mlir-optand such.pythontop level directory for Python codeDevelopersIf you would like to develop and build torch-mlir from source please look atDevelopment Notes