Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/llvm/torch-mlir

The Torch-MLIR project aims to provide first class support from the PyTorch ecosystem to the MLIR ecosystem.
https://github.com/llvm/torch-mlir

compiler mlir pytorch

Last synced: 29 days ago
JSON representation

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

Awesome Lists containing this project

README 

        
# The Torch-MLIR Project



The Torch-MLIR project aims to provide first class compiler support from the [PyTorch](https://pytorch.org) 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](https://pytorch.org)

PyTorch is an open source machine learning framework that facilitates the seamless transition from research and prototyping to production-level deployment.



[MLIR](https://mlir.llvm.org)

The MLIR project offers a novel approach for building extensible and reusable compiler architectures, which address the issue of software fragmentation, reduce the cost of developing domain-specific compilers, improve compilation for heterogeneous hardware, and promote compatibility between existing compilers.



[Torch-MLIR](https://github.com/llvm/torch-mlir)

Several vendors have adopted MLIR as the middle layer in their systems, enabling them to map frameworks such as PyTorch, JAX, and TensorFlow into MLIR and subsequently lower them to their target hardware. We have observed half a dozen custom lowerings from PyTorch to MLIR, making it easier for hardware vendors to focus on their unique value, rather than needing to implement yet another PyTorch frontend for MLIR. The ultimate aim is to be similar to the current hardware vendors adding LLVM target support, rather than each one implementing Clang or a C++ frontend.



[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)



## All the roads from PyTorch to Torch MLIR Dialect



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

 - ONNX as the entry points.

 - Fx as the entry points



## Project Communication



- `#torch-mlir` channel on the LLVM [Discord](https://discord.gg/xS7Z362) - this is the most active communication channel

- Github issues [here](https://github.com/llvm/torch-mlir/issues)

- [`torch-mlir` section](https://llvm.discourse.group/c/projects-that-want-to-become-official-llvm-projects/torch-mlir/41) of LLVM Discourse



## Install torch-mlir snapshot



At the time of writing, we release [pre-built snapshots of torch-mlir](https://github.com/llvm/torch-mlir-release) for Python 3.11 and Python 3.10.



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

```shell

python3.11 -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 3.11.

```shell

conda create -n torch-mlir python=3.11

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 \

  --extra-index-url https://download.pytorch.org/whl/nightly/cpu \

  -f https://github.com/llvm/torch-mlir-release/releases/expanded_assets/dev-wheels

```



## Using torch-mlir



Torch-MLIR is primarily a project that is integrated into compilers to bridge them to PyTorch and ONNX. If contemplating a new integration, it may be helpful to refer to existing downstreams:



* [IREE](https://github.com/iree-org/iree.git)

* [Blade](https://github.com/alibaba/BladeDISC)



While most of the project is exercised via testing paths, there are some ways that an end user can directly use the APIs without further integration:



### FxImporter ResNet18

```shell

# Get the latest example if you haven't checked out the code

wget https://raw.githubusercontent.com/llvm/torch-mlir/main/projects/pt1/examples/fximporter_resnet18.py



# Run ResNet18 as a standalone script.

python projects/pt1/examples/fximporter_resnet18.py



# Output

load image from https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg

...

PyTorch prediction

[('Labrador retriever', 70.65674591064453), ('golden retriever', 4.988346099853516), ('Saluki, gazelle hound', 4.477451324462891)]

torch-mlir prediction

[('Labrador retriever', 70.6567153930664), ('golden retriever', 4.988325119018555), ('Saluki, gazelle hound', 4.477458477020264)]

```



## Repository Layout



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



## Developers

If you would like to develop and build torch-mlir from source please look at [Development Notes](docs/development.md)