An open source machine learning framework that accelerates the path from research prototyping to production deployment.
[MLIR](https://mlir.llvm.org)
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.
Multiple Vendors use MLIR as the middle layer mapping from platform frameworks like PyTorch, JAX, TensorFlow onto MLIR and then progressively lower 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 another PyTorch frontend for MLIR. It would be similar to current hardware vendors adding LLVM target support instead of each one also implementing the Clang/C++ frontend.
## All the roads from PyTorch to Torch MLIR Dialect
We have few paths to lower down to the Torch MLIR Dialect.
![Torch Lowering Architectures](Torch-MLIR.png)
- Torchscript
This is the most tested path down to Torch MLIR Dialect.
- TorchFX
This provides a path to lower from TorchFX down to MLIR. This a functional prototype that we expect to mature as TorchFX matures
- Lazy Tensor Core (Based on lazy-tensor-core [staging branch](https://github.com/pytorch/pytorch/tree/lazy_tensor_staging/lazy_tensor_core))
This path provides the upcoming LTC path of capture. It is based of an unstable devel branch but is the closest way for you to adapt any existing torch_xla derivatives.
- “ACAP” - Deprecated torch_xla based capture Mentioned here for completeness.