# How to Add Ops to Torch-Mlir Collected links and contacts for how to add ops to torch-mlir.
Turbine Camp: Start Here This document was previously known as `turbine-camp.md` to Nod.ai. "Turbine Camp" is part of Nod.ai's onboarding process. Welcome to turbine camp. This document originated at Nod.ai as a part of onboardding process, where new nod-ai folks learn about the architecture of our work by adding support for 2 ops to torch-mlir. I decided to put this into torch mlir because a lot of this is about torch-mlir. Written & maintained by @renxida Guides by other folks that were used during the creation of this document: - [Chi Liu](https://gist.github.com/AmosLewis/dd31ab37517977b1c499d06495b4adc2) - [Sunsoon](https://docs.google.com/document/d/1H79DwW_wnVzUU81EogwY5ueXgnl-QzKet1p2lnqPar4/edit?pli=1) ## Before you begin... Nod-ai maintains the pipeline below, which allows us to take a ML model from e.g. huggingface, and compile it to a variety of devices including llvm-cpu, rocm and cuda and more as an optimized `vmfb` binary. 1. The pipeline begins with a huggingface model, or some other supported source like llama.cpp. 2. [nod-ai/SHARK-Turbine](https://github.com/nod-ai/SHARK-Turbine) takes a huggingface model and exports a `.mlir` file. 3. **[llvm/torch-mlir](https://github.com/llvm/torch-mlir)**, which you will be working on in turbine-camp, will lower torchscript, torch dialect, and torch aten ops further into a mixture `linalg` or `math` MLIR dialects (with occasionally other dialects in the mix) 4. [IREE](https://github.com/openxla/iree) converts the final `.mlir` file into a binary (typically `.vmfb`) for running on a device (llvm-cpu, rocm, vulcan, cuda, etc). The details of how we do it and helpful commands to help you set up each repo is in [Sungsoon's Shark Getting Started Google Doc](https://docs.google.com/document/d/1H79DwW_wnVzUU81EogwY5ueXgnl-QzKet1p2lnqPar4/edit?pli=1) PS: IREE is pronounced Eerie, and hence the ghost icon. ## How to begin 1. You will start by adding support for 2 ops in torch-mlir, to get you familiar with the center of our pipeline. Begin by reading [torch-mlir's documentation on how to implement a new torch op](https://github.com/llvm/torch-mlir/blob/main/docs/Torch-ops-E2E-implementation.md), and set up `llvm/torch_mlir` using https://github.com/llvm/torch-mlir/blob/main/docs/development.md 2. Pick 1 of the yet-unimplemented from the following. You should choose something that looks easy to you. **Make sure you create an issue by clicking the little "target" icon to the right of the op, thereby marking the op as yours** - [TorchToLinalg ops tracking issue](https://github.com/nod-ai/SHARK-Turbine/issues/347) - [TorchOnnnxToTorch ops tracking issue](https://github.com/nod-ai/SHARK-Turbine/issues/215) 3. Implement it. For torch -> linalg, see the how to torchop section below. For Onnx ops, see how to onnx below. 5. Make a pull request and reference your issue. When the pull request is closed, also close your issue to mark the op as done
### How to TorchToLinalg You will need to do 4 things: - make sure the op exists in `torch_ods_gen.py`, and then run `build_tools/update_torch_ods.sh`, and then build. This generates `GeneratedTorchOps.td`, which is used to generate the cpp and h files where ops function signatures are defined. - Reference [torch op registry](https://github.com/pytorch/pytorch/blob/7451dd058564b5398af79bfc1e2669d75f9ecfa2/torch/csrc/jit/passes/utils/op_registry.cpp#L21) - make sure the op exists in `abstract_interp_lib_gen.py`, and then run `build_tools/update_abstract_interp_lib.sh`, and then build. This generates `AbstractInterpLib.cpp`, which is used to generate the cpp and h files where ops function signatures are defined. - Reference [torch shape functions](https://github.com/pytorch/pytorch/blob/7451dd058564b5398af79bfc1e2669d75f9ecfa2/torch/jit/_shape_functions.py#L1311) - write test cases. They live in `projects/pt1`. See the [Dec 2023 example](https://github.com/llvm/torch-mlir/pull/2640/files). - implement the op in one of the `lib/Conversion/TorchToLinalg/*.cpp` files Reference Examples - [A Dec 2023 example with the most up to date lowering](https://github.com/llvm/torch-mlir/pull/2640/files) - [Chi's simple example of adding op lowering](https://github.com/llvm/torch-mlir/pull/1454) useful instructions and referring links for you to understand the op lowering pipeline in torch-mlir in the comments Resources: - how to set up torch-mlir: [https://github.com/llvm/torch-mlir/blob/main/docs/development.md](https://github.com/llvm/torch-mlir/blob/main/docs/development.md#checkout-and-build-from-source) - torch-mlir doc on how to debug and test: [ttps://github.com/llvm/torch-mlir/blob/main/docs/development.md#testing](https://github.com/llvm/torch-mlir/blob/main/docs/development.md#testing) - [torch op registry](https://github.com/pytorch/pytorch/blob/7451dd058564b5398af79bfc1e2669d75f9ecfa2/torch/csrc/jit/passes/utils/op_registry.cpp#L21) - [torch shape functions](https://github.com/pytorch/pytorch/blob/7451dd058564b5398af79bfc1e2669d75f9ecfa2/torch/jit/_shape_functions.py#L1311) ### How to TorchOnnxToTorch 0. Generate the big folder of ONNX IR. Use https://github.com/llvm/torch-mlir/blob/main/test/python/onnx_importer/import_smoke_test.py . Alternatively, if you're trying to support a certain model, convert that model to onnx IR with ``` optimum-cli export onnx --model facebook/opt-125M fb-opt python -m torch_mlir.tools.import_onnx fb-opt/model.onnx -o fb-opt-125m.onnx.mlir ``` 2. Find an instance of the Op that you're trying to implement inside the smoke tests folder or the generated model IR, and write a test case. Later you will save it to one of the files in `torch-mlir/test/Conversion/TorchOnnxToTorch`, but for now feel free to put it anywhere. 3. Implement the op in `lib/Conversion/TorchOnnxToTorch/something.cpp`. 4. Test the conversion by running `./build/bin/torch-mlir-opt -split-input-file -verify-diagnostics -convert-torch-onnx-to-torch your_mlir_file.mlir`. For more details, see https://github.com/llvm/torch-mlir/blob/main/docs/development.md#testing . Xida usually creates a separate MLIR file to test it to his satisfaction before integrating it into one of the files at `torch-mlir/test/Conversion/TorchOnnxToTorch`. Helpful examples: - [A Dec 2023 example where an ONNX op is implemented](https://github.com/llvm/torch-mlir/pull/2641/files#diff-b584b152020af6d2e5dbf62a08b2f25ed5afc2c299228383b9651d22d44b5af4R493) - [Vivek's example of ONNX op lowering](https://github.com/llvm/torch-mlir/commit/dc9ea08db5ac295b4b3f91fc776fef6a702900b9) ## Contacts People who've worked on this for a while - Vivek (@vivek97 on discord) - Chi.Liu@amd.com Recent Turbine Camp Attendees, from recent to less recent - Xida.ren@amd.com (@xida_ren on discord) - Sungsoon.Cho@amd.com ## Links - Tutorials - [Sungsoon's Shark Getting Started Google Doc](https://docs.google.com/document/d/1H79DwW_wnVzUU81EogwY5ueXgnl-QzKet1p2lnqPar4/edit?pli=1) - This document contains commands that would help you set up shark and run demos - [How to implement ONNX op lowering](https://github.com/llvm/torch-mlir/blob/main/docs/importers/onnx_importer.md) - Examples - [A Dec 2023 example with the most up to date lowering](https://github.com/llvm/torch-mlir/pull/2640/files) - Chi's Example Lowering - Github issue and code detailing how to implement the lowring of an OP. - [Chi's simple example of adding op lowering](https://github.com/llvm/torch-mlir/pull/1454) useful instructions and referring links for you to understand the op lowering pipeline in torch-mlir in the comments - If you have questions, reach out to [Chi on Discord](https://discordapp.com/channels/973663919757492264/1104195883307892837/1180233875058868224) - [Vivek's example of ONNX op lowering](https://github.com/llvm/torch-mlir/commit/dc9ea08db5ac295b4b3f91fc776fef6a702900b9) - Find Ops To Lower - [Torch MLIR + ONNX Unimplemented Ops on Sharepoint](https://amdcloud-my.sharepoint.com/:x:/r/personal/esaimana_amd_com/Documents/Torch%20MLIR%20+%20ONNX%20Unimplemented%20Ops.xlsx?d=w438f26fac8fd44eeafb89bc99e2c563b&csf=1&web=1&e=Qd4eHm) - If you don't have access yet, request it. - nod-ai/SHARK-Turbine ssues tracking op support - [Model and Op Support](https://github.com/nod-ai/SHARK-Turbine/issues/119) - [ONNX op support](https://github.com/nod-ai/SHARK-Turbine/issues/215) ## Chi's useful commands for debugging torch mlir https://gist.github.com/AmosLewis/dd31ab37517977b1c499d06495b4adc2 ## How to write test cases and test your new op https://github.com/llvm/torch-mlir/blob/main/docs/development.md#testing ## How to set up vs code and intellisence for [torch-mlir] Xida: This is optional. If you're using VS code like me, you might want to set it up so you can use the jump to definition / references, auto fix, and other features. Feel free to contact me on discord if you have trouble figuring this out. You may need to write something like this into your ```.vscode/settings.json``` under `torch-mlir` ```json { "files.associations": { "*.inc": "cpp", "ranges": "cpp", "regex": "cpp", "functional": "cpp", "chrono": "cpp", "__functional_03": "cpp", "target": "cpp" }, "cmake.sourceDirectory": ["/home/xida/torch-mlir/externals/llvm-project/llvm"], "cmake.buildDirectory": "${workspaceFolder}/build", "cmake.generator": "Ninja", "cmake.configureArgs": [ "-DLLVM_ENABLE_PROJECTS=mlir", "-DLLVM_EXTERNAL_PROJECTS=\"torch-mlir\"", "-DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR=\"/home/xida/torch-mlir\"", "-DCMAKE_BUILD_TYPE=Release", "-DCMAKE_C_COMPILER_LAUNCHER=ccache", "-DCMAKE_CXX_COMPILER_LAUNCHER=ccache", "-DLLVM_ENABLE_PROJECTS=mlir", "-DLLVM_EXTERNAL_PROJECTS=torch-mlir", "-DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR=${workspaceFolder}", "-DMLIR_ENABLE_BINDINGS_PYTHON=ON", "-DLLVM_ENABLE_ASSERTIONS=ON", "-DLLVM_TARGETS_TO_BUILD=host", ], "C_Cpp.default.configurationProvider": "ms-vscode.cmake-tools", "cmake.configureEnvironment": { "PATH": "/home/xida/miniconda/envs/torch-mlir/bin:/home/xida/miniconda/condabin:/home/xida/miniconda/bin:/home/xida/miniconda/bin:/home/xida/miniconda/condabin:/home/xida/miniconda/bin:/home/xida/miniconda/bin:/home/xida/miniconda/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin" }, "cmake.cmakePath": "/home/xida/miniconda/envs/torch-mlir/bin/cmake", // make sure this is a cmake that knows where your python is } ``` The important things to note are the `cmake.configureArgs`, which specify the location of your torch mlir, and the `cmake.sourceDirectory`, which indicates that CMAKE should not build from the current directory and should instead build from `externals/llvm-project/llvm`