[AutoBump] Merge with fixes of 13ee7c21 (Dec 19) (143)

.github/workflows/ci.yml

@@ -27,16 +27,6 @@ jobs:
     env:
       CACHE_DIR: ${{ github.workspace }}/.container-cache
     steps:
-      - name: Configure local git mirrors
-        run: |
-          # Our stock runners have access to certain local git caches. If these
-          # files are available, it will prime the cache and configure git to
-          # use them. Practically, this eliminates network/latency for cloning
-          # llvm.
-          if [[ -x /gitmirror/scripts/trigger_update_mirrors.sh ]]; then
-            /gitmirror/scripts/trigger_update_mirrors.sh
-            /gitmirror/scripts/git_config.sh
-          fi
       - name: "Checking out repository"
         uses: actions/checkout@8f4b7f84864484a7bf31766abe9204da3cbe65b3 # v3.5.0
         with:
@@ -55,11 +45,25 @@ jobs:
           restore-keys: |
             build-test-cpp-asserts-manylinux-${{ matrix.torch-version }}-v2-
 
+      - name: "Setting up Python"
+        run: |
+          sudo apt update
+          sudo apt install software-properties-common -y
+          sudo add-apt-repository ppa:deadsnakes/ppa -y
+          sudo apt install python3.11 python3-pip -y
+          sudo apt-get install python3.11-dev python3.11-venv build-essential -y
+
       - name: Install python deps (torch-${{ matrix.torch-version }})
         run: |
           export cache_dir="${{ env.CACHE_DIR }}"
           bash build_tools/ci/install_python_deps.sh ${{ matrix.torch-version }}
 
+      - name: ccache
+        uses: hendrikmuhs/ccache-action@53911442209d5c18de8a31615e0923161e435875 # v1.2.16
+        with:
+          key: ${{ github.job }}-${{ matrix.torch-version }}
+          save: ${{ needs.setup.outputs.write-caches == 1 }}
+
       - name: Build project
         run: |
           export cache_dir="${{ env.CACHE_DIR }}"

CMakeLists.txt

@@ -43,6 +43,12 @@ option(TORCH_MLIR_ENABLE_STABLEHLO "Add stablehlo dialect" ON)
 if(TORCH_MLIR_ENABLE_STABLEHLO)
   add_definitions(-DTORCH_MLIR_ENABLE_STABLEHLO)
 endif()
+# It is possible that both stablehlo and torch_mlir projects are used in some compiler project.
+# In this case, we don't want to use stablehlo that is downloaded by torch_mlir (in external/stablehlo)
+# folder but instead want to use stablehlo that is part of top level compiler project.
+# With TORCH_MLIR_USE_EXTERNAL_STABLEHLO enables, it is assumed that top level compiler project makes
+# stablehlo targets AND includes available (for example with `add_subdirectory` and `include_directories`).
+option(TORCH_MLIR_USE_EXTERNAL_STABLEHLO "Use stablehlo from top level project" OFF)
 
 option(TORCH_MLIR_OUT_OF_TREE_BUILD "Specifies an out of tree build" OFF)
 
@@ -142,7 +148,8 @@ include_directories(${CMAKE_CURRENT_BINARY_DIR}/include)
 
 function(torch_mlir_target_includes target)
   set(_dirs
-    $<BUILD_INTERFACE:${MLIR_INCLUDE_DIRS}>
+    $<BUILD_INTERFACE:${MLIR_INCLUDE_DIR}>
+    $<BUILD_INTERFACE:${MLIR_GENERATED_INCLUDE_DIR}>
     $<BUILD_INTERFACE:${TORCH_MLIR_SOURCE_DIR}/include>
     $<BUILD_INTERFACE:${TORCH_MLIR_BINARY_DIR}/include>
   )
@@ -232,7 +239,8 @@ endif()
 # Getting this wrong results in building large parts of the stablehlo
 # project that we don't actually depend on. Further some of those parts
 # do not even compile on all platforms.
-if (TORCH_MLIR_ENABLE_STABLEHLO)
+# Only configure StableHLO if it isn't provided from a top-level project
+if (TORCH_MLIR_ENABLE_STABLEHLO AND NOT TORCH_MLIR_USE_EXTERNAL_STABLEHLO)
   set(STABLEHLO_BUILD_EMBEDDED ON)
   set(STABLEHLO_ENABLE_BINDINGS_PYTHON ON)
   add_subdirectory(${CMAKE_CURRENT_SOURCE_DIR}/externals/stablehlo

README.md

@@ -60,7 +60,6 @@ Torch-MLIR is primarily a project that is integrated into compilers to bridge th
 
 * [IREE](https://github.com/iree-org/iree.git)
 * [Blade](https://github.com/alibaba/BladeDISC)
-* [MPACT](https://github.com/MPACT-ORG/mpact-compiler)
 
 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:
 

build_tools/ci/build_posix.sh

@@ -20,7 +20,7 @@ echo "Caching to ${cache_dir}"
 mkdir -p "${cache_dir}/ccache"
 mkdir -p "${cache_dir}/pip"
 
-python="$(which python)"
+python="$(which python3)"
 echo "Using python: $python"
 
 export CMAKE_TOOLCHAIN_FILE="$this_dir/linux_default_toolchain.cmake"
@@ -40,7 +40,7 @@ echo "::group::CMake configure"
 cmake -S "$repo_root/externals/llvm-project/llvm" -B "$build_dir" \
   -GNinja \
   -DCMAKE_BUILD_TYPE=Release \
-  -DPython3_EXECUTABLE="$(which python)" \
+  -DPython3_EXECUTABLE="$(which python3)" \
   -DLLVM_ENABLE_ASSERTIONS=ON \
   -DTORCH_MLIR_ENABLE_WERROR_FLAG=ON \
   -DCMAKE_INSTALL_PREFIX="$install_dir" \

build_tools/ci/install_python_deps.sh

@@ -7,7 +7,7 @@ repo_root="$(cd $this_dir/../.. && pwd)"
 torch_version="${1:-unknown}"
 
 echo "::group::installing llvm python deps"
-python -m pip install --no-cache-dir -r $repo_root/externals/llvm-project/mlir/python/requirements.txt
+python3 -m pip install --no-cache-dir -r $repo_root/externals/llvm-project/mlir/python/requirements.txt
 echo "::endgroup::"
 
 case $torch_version in
@@ -30,5 +30,5 @@ case $torch_version in
 esac
 
 echo "::group::installing test requirements"
-python -m pip install --no-cache-dir -r $repo_root/test-requirements.txt
+python3 -m pip install --no-cache-dir -r $repo_root/test-requirements.txt
 echo "::endgroup::"

build_tools/ci/test_posix.sh

@@ -13,7 +13,7 @@ python -m e2e_testing.main --config=fx_importer_tosa -v
 echo "::endgroup::"
 
 echo "::group::Run ONNX e2e integration tests"
-python -m e2e_testing.main --config=onnx -v
+python3 -m e2e_testing.main --config=onnx -v
 echo "::endgroup::"
 
 case $torch_version in
@@ -27,13 +27,13 @@ case $torch_version in
 
     # TODO: Need to verify in the stable version
     echo "::group::Run FxImporter e2e integration tests"
-    python -m e2e_testing.main --config=fx_importer -v
+    python3 -m e2e_testing.main --config=fx_importer -v
     echo "::endgroup::"
 
     # AMD: Disabled stablehlo.
     # TODO: Need to verify in the stable version
     # echo "::group::Run FxImporter2Stablehlo e2e integration tests"
-    # python -m e2e_testing.main --config=fx_importer_stablehlo -v
+    # python3 -m e2e_testing.main --config=fx_importer_stablehlo -v
     # echo "::endgroup::"
     ;;
   stable)

build_tools/python_deploy/build_linux_packages.sh

@@ -324,9 +324,6 @@ function test_in_tree() {
       ;;
     esac
 
-  echo ":::: Run make_fx + TOSA e2e integration tests"
-  python -m e2e_testing.main --config=make_fx_tosa -v
-
   echo ":::: Run TOSA e2e integration tests"
   python -m e2e_testing.main --config=tosa -v
 }

build_tools/update_abstract_interp_lib.sh

@@ -44,7 +44,7 @@ fi
 # To enable this python package, manually build torch_mlir with:
 #   -DTORCH_MLIR_ENABLE_JIT_IR_IMPORTER=ON
 # TODO: move this package out of JIT_IR_IMPORTER.
-PYTHONPATH="${pypath}" python \
+PYTHONPATH="${pypath}" python3 \
   -m torch_mlir.jit_ir_importer.build_tools.abstract_interp_lib_gen \
   --pytorch_op_extensions=${ext_module:-""} \
   --torch_transforms_cpp_dir="${torch_transforms_cpp_dir}"

build_tools/update_torch_ods.sh

@@ -45,7 +45,7 @@ set +u
 # To enable this python package, manually build torch_mlir with:
 #   -DTORCH_MLIR_ENABLE_PYTORCH_EXTENSIONS=ON
 # TODO: move this package out of JIT_IR_IMPORTER.
-PYTHONPATH="${PYTHONPATH}:${pypath}" python \
+PYTHONPATH="${PYTHONPATH}:${pypath}" python3 \
   -m torch_mlir.jit_ir_importer.build_tools.torch_ods_gen \
   --torch_ir_include_dir="${torch_ir_include_dir}" \
   --pytorch_op_extensions="${ext_module}" \

docs/add_ops.md

@@ -2,72 +2,49 @@
 
 Collected links and contacts for how to add ops to torch-mlir.
 
-<details>
-<summary>Turbine Camp: Start Here</summary>
-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.
+## [How to Add a Torch Operator](https://github.com/llvm/torch-mlir/blob/main/docs/Torch-ops-E2E-implementation.md)
 
-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
-0. Set up torch-mlir according to the instructions here: https://github.com/llvm/torch-mlir/blob/main/docs/development.md
-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
-
-</details>
+## How to Add a Conversion for an Operator
 
 ### How to TorchToLinalg
 
 You will need to do 5 things:
+
 - make sure -DTORCH_MLIR_ENABLE_JIT_IR_IMPORTER=ON is added during build. This is to enable the python file used in `build_tools/update_torch_ods.sh` and `build_tools/update_abstract_interp_lib.sh`
 - 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)
+  - 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)
+  - 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)
+
+- [how to set up torch-mlir](https://github.com/llvm/torch-mlir/blob/main/docs/development.md)
+- [torch-mlir doc on how to debug and test](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
-   ```
+
+1. Generate the big folder of ONNX IR. Use [this Python script](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
+
+   ```shell
    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`.
+
+1. 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.
+1. Implement the op in `lib/Conversion/TorchOnnxToTorch/something.cpp`.
+1. 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 [the testing section of the doc on development](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)
 
@@ -77,7 +54,8 @@ Helpful examples:
 `. Please don't just paste the generated tests - reference them to write your own
 
 ## Links
-- IMPORTANT: read the LLVM style guide: https://llvm.org/docs/CodingStandards.html#use-early-exits-and-continue-to-simplify-code
+
+- IMPORTANT: read [the LLVM style guide](https://llvm.org/docs/CodingStandards.html#style-issues)
 - 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
@@ -96,18 +74,12 @@ Helpful examples:
     - [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)
 
-## 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 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.
@@ -153,4 +125,5 @@ under `torch-mlir`
     "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`