Disable TORCH_MLIR_ENABLE_JIT_IR_IMPORTER and TORCH_MLIR_ENABLE_PYTORCH_EXTENSIONS by default (#3693)

Only enable it in CI and debug for update_abstract_interp_lib.sh and update_torch_ods.sh usage.

Author: AmosLewis

CMakeLists.txt

@@ -47,8 +47,14 @@ endif()
 option(TORCH_MLIR_OUT_OF_TREE_BUILD "Specifies an out of tree build" OFF)
 
 # PyTorch native extension gate. If OFF, then no features which depend on
-# native extensions will be built.
-option(TORCH_MLIR_ENABLE_PYTORCH_EXTENSIONS "Enables PyTorch native extension features" ON)
+# native extensions will be built.TORCH_MLIR_ENABLE_PYTORCH_EXTENSIONS is disabled by default.
+# But it will be manually enabled in CI build to enable the jit_ir_importer.build_tools.torch_ods_gen
+# and abstract_interp_lib_gen.py. Once pure python version of build_tools finished, no need to set it in CI.
+option(TORCH_MLIR_ENABLE_PYTORCH_EXTENSIONS "Enables PyTorch native extension features" OFF)
+# NOTE: The JIT_IR_IMPORTER paths have become unsupportable due to age and lack of maintainers.
+# Turning this off disables the old TorchScript path, leaving FX based import as the current supported option.
+# The option will be retained for a time, and if a maintainer is interested in setting up testing for it,
+# please reach out on the list and speak up for it. It will only be enabled in CI for test usage.
 cmake_dependent_option(TORCH_MLIR_ENABLE_JIT_IR_IMPORTER "Enables JIT IR Importer" ON TORCH_MLIR_ENABLE_PYTORCH_EXTENSIONS OFF)
 cmake_dependent_option(TORCH_MLIR_ENABLE_LTC "Enables LTC backend" OFF TORCH_MLIR_ENABLE_PYTORCH_EXTENSIONS OFF)
 

README.md

@@ -21,35 +21,15 @@ Several vendors have adopted MLIR as the middle layer in their systems, enabling
 ## All the roads from PyTorch to Torch MLIR Dialect
 
 We have few paths to lower down to the Torch MLIR Dialect.
-
-![Simplified Architecture Diagram for README](docs/images/readme_architecture_diagram.png)
-
- - TorchScript
-    This is the most tested path down to Torch MLIR Dialect.
- - LazyTensorCore
-    Read more details [here](docs/ltc_backend.md).
- - We also have basic TorchDynamo/PyTorch 2.0 support, see our
-   [long-term roadmap](docs/roadmap.md) and
-   [Thoughts on PyTorch 2.0](https://discourse.llvm.org/t/thoughts-on-pytorch-2-0/67000/3)
-   for more details.
+ - 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
 
-### Meetings
-
-Community Meeting / Developer Hour:
-- 1st and 3rd Monday of the month at 9 am PST
-- 2nd and 4th Monday of the month at 5 pm PST
-
-Office Hours:
-- Every Thursday at 8:30 am PST
-
-Meeting links can be found [here](https://discourse.llvm.org/t/new-community-meeting-developer-hour-schedule/73868).
-
 ## 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.
@@ -74,7 +54,14 @@ pip install --pre torch-mlir torchvision \
   -f https://github.com/llvm/torch-mlir-release/releases/expanded_assets/dev-wheels
 ```
 
-## Demos
+## 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
@@ -93,30 +80,6 @@ torch-mlir prediction
 [('Labrador retriever', 70.6567153930664), ('golden retriever', 4.988325119018555), ('Saluki, gazelle hound', 4.477458477020264)]
 ```
 
-### TorchScript ResNet18
-
-Standalone script to Convert a PyTorch ResNet18 model to MLIR and run it on the CPU Backend:
-
-```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/torchscript_resnet18.py
-
-# Run ResNet18 as a standalone script.
-python projects/pt1/examples/torchscript_resnet18.py
-
-load image from https://upload.wikimedia.org/wikipedia/commons/2/26/YellowLabradorLooking_new.jpg
-Downloading: "https://download.pytorch.org/models/resnet18-f37072fd.pth" to /home/mlir/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth
-100.0%
-PyTorch prediction
-[('Labrador retriever', 70.66319274902344), ('golden retriever', 4.956596374511719), ('Chesapeake Bay retriever', 4.195662975311279)]
-torch-mlir prediction
-[('Labrador retriever', 70.66320037841797), ('golden retriever', 4.956601619720459), ('Chesapeake Bay retriever', 4.195651531219482)]
-```
-
-### Lazy Tensor Core
-
-View examples [here](docs/ltc_examples.md).
-
 ## Repository Layout
 
 The project follows the conventions of typical MLIR-based projects:

build_tools/ci/build_posix.sh

@@ -50,7 +50,8 @@ cmake -S "$repo_root/externals/llvm-project/llvm" -B "$build_dir" \
   -DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR="$repo_root" \
   -DLLVM_TARGETS_TO_BUILD=host \
   -DMLIR_ENABLE_BINDINGS_PYTHON=ON \
-  -DTORCH_MLIR_ENABLE_LTC=ON
+  -DTORCH_MLIR_ENABLE_LTC=ON \
+  -DTORCH_MLIR_ENABLE_PYTORCH_EXTENSIONS=ON
 echo "::endgroup::"
 
 echo "::group::Build"

build_tools/ci/test_posix.sh

@@ -8,22 +8,6 @@ torch_version="${1:-unknown}"
 
 export PYTHONPATH="$repo_root/build/tools/torch-mlir/python_packages/torch_mlir:$repo_root/projects/pt1"
 
-echo "::group::Run Linalg e2e integration tests"
-python -m e2e_testing.main --config=linalg -v
-echo "::endgroup::"
-
-echo "::group::Run make_fx + TOSA e2e integration tests"
-python -m e2e_testing.main --config=make_fx_tosa -v
-echo "::endgroup::"
-
-echo "::group::Run TOSA e2e integration tests"
-python -m e2e_testing.main --config=tosa -v
-echo "::endgroup::"
-
-echo "::group::Run Stablehlo e2e integration tests"
-python -m e2e_testing.main --config=stablehlo -v
-echo "::endgroup::"
-
 echo "::group::Run ONNX e2e integration tests"
 python -m e2e_testing.main --config=onnx -v
 echo "::endgroup::"

build_tools/update_abstract_interp_lib.sh

@@ -41,6 +41,9 @@ if [ ! -z ${TORCH_MLIR_EXT_MODULES} ]; then
   ext_module="${TORCH_MLIR_EXT_MODULES} "
 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="${pypath}" python \
   -m torch_mlir.jit_ir_importer.build_tools.abstract_interp_lib_gen \
   --pytorch_op_extensions=${ext_module:-""} \

build_tools/update_torch_ods.sh

@@ -42,6 +42,9 @@ if [ ! -z ${TORCH_MLIR_EXT_MODULES} ]; then
 fi
 
 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 \
   -m torch_mlir.jit_ir_importer.build_tools.torch_ods_gen \
   --torch_ir_include_dir="${torch_ir_include_dir}" \

docs/add_ops.md

@@ -38,7 +38,8 @@ PS: IREE is pronounced Eerie, and hence the ghost icon.
 
 ### How to TorchToLinalg
 
-You will need to do 4 things:
+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)
 - 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.