llvm
/
torch-mlir
mirror of https://github.com/llvm/torch-mlir
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

[README] Small touch-ups, and mention PT2

pull/1716/head
Sean Silva 2022-12-13 14:28:23 +00:00
parent 8d098dc8d5
commit 2acf7da63c
1 changed files with 7 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
README.md
View File

@ -14,7 +14,7 @@ An open source machine learning framework that accelerates the path from researc
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. 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](https://github.com/llvm/torch-mlir) [Torch-MLIR](https://github.com/llvm/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 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. 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.
[![Release Build](https://github.com/llvm/torch-mlir/actions/workflows/buildRelease.yml/badge.svg)](https://github.com/llvm/torch-mlir/actions/workflows/buildRelease.yml) [![Release Build](https://github.com/llvm/torch-mlir/actions/workflows/buildRelease.yml/badge.svg)](https://github.com/llvm/torch-mlir/actions/workflows/buildRelease.yml)
@ -25,9 +25,14 @@ We have few paths to lower down to the Torch MLIR Dialect.
![Simplified Architecture Diagram for README](docs/images/readme_architecture_diagram.png) ![Simplified Architecture Diagram for README](docs/images/readme_architecture_diagram.png)
- TorchScript - 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. This is the most tested path down to Torch MLIR Dialect.
- LazyTensorCore - LazyTensorCore
Read more details [here](docs/ltc_backend.md). Read more details [here](docs/ltc_backend.md).
- We also have basic TorchDynamo/PyTorch 2.0 support, see our
[long-term roadmap](docs/long_term_roadmap.md) and
[Thoughts on PyTorch 2.0](https://discourse.llvm.org/t/thoughts-on-pytorch-2-0/67000/3)
for more details.
## Project Communication ## Project Communication
- `#torch-mlir` channel on the LLVM [Discord](https://discord.gg/xS7Z362) - this is the most active communication channel - `#torch-mlir` channel on the LLVM [Discord](https://discord.gg/xS7Z362) - this is the most active communication channel