Skip to content

Latest commit

 

History

History
244 lines (181 loc) · 8.63 KB

development.md

File metadata and controls

244 lines (181 loc) · 8.63 KB
Raw

Checkout and build from source

Check out the code

git clone https://github.com/llvm/torch-mlir
cd torch-mlir
git submodule update --init

Setup your Python VirtualEnvironment and Dependencies

Also, ensure that you have the appropriate python-dev package installed to access the Python development libraries / headers.

python -m venv mlir_venv
source mlir_venv/bin/activate
# Some older pip installs may not be able to handle the recent PyTorch deps
python -m pip install --upgrade pip
# Install latest PyTorch nightlies and build requirements.
python -m pip install -r requirements.txt

Build Python Packages

We have preliminary support for building Python packages. This can be done with the following commands:

python -m pip install --upgrade pip
python -m pip install -r requirements.txt
CMAKE_GENERATOR=Ninja python setup.py bdist_wheel

CMake Build

Two setups are possible to build: in-tree and out-of-tree. The in-tree setup is the most straightforward, as it will build LLVM dependencies as well.

Building torch-mlir in-tree

The following command generates configuration files to build the project in-tree, that is, using llvm/llvm-project as the main build. This will build LLVM as well as torch-mlir and its subprojects.

cmake -GNinja -Bbuild \
  -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_C_COMPILER=clang \
  -DCMAKE_CXX_COMPILER=clang++ \
  -DPython3_FIND_VIRTUALENV=ONLY \
  -DLLVM_ENABLE_PROJECTS=mlir \
  -DLLVM_EXTERNAL_PROJECTS="torch-mlir;torch-mlir-dialects" \
  -DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR=`pwd` \
  -DLLVM_EXTERNAL_TORCH_MLIR_DIALECTS_SOURCE_DIR=`pwd`/externals/llvm-external-projects/torch-mlir-dialects \
  -DMLIR_ENABLE_BINDINGS_PYTHON=ON \
  -DLLVM_TARGETS_TO_BUILD=host \
  externals/llvm-project/llvm

The following additional quality of life flags can be used to reduce build time:

  • Enabling ccache:
  -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache
  • Enabling LLD (links in seconds compared to minutes)
  -DCMAKE_EXE_LINKER_FLAGS_INIT="-fuse-ld=lld" -DCMAKE_MODULE_LINKER_FLAGS_INIT="-fuse-ld=lld" -DCMAKE_SHARED_LINKER_FLAGS_INIT="-fuse-ld=lld"
# Use --ld-path= instead of -fuse-ld=lld for clang > 13

Building against a pre-built LLVM

If you have built llvm-project separately in the directory $LLVM_INSTALL_DIR, you can also build the project out-of-tree using the following command as template:

cmake -GNinja -Bbuild \
  -DCMAKE_BUILD_TYPE=Release \
  -DCMAKE_C_COMPILER=clang \
  -DCMAKE_CXX_COMPILER=clang++ \
  -DPython3_FIND_VIRTUALENV=ONLY \
  -DMLIR_DIR="$LLVM_INSTALL_DIR/lib/cmake/mlir/" \
  -DLLVM_DIR="$LLVM_INSTALL_DIR/lib/cmake/llvm/" \
  -DMLIR_ENABLE_BINDINGS_PYTHON=ON \
  -DLLVM_TARGETS_TO_BUILD=host \
  .

The same QoL CMake flags can be used to enable ccache and lld. Be sure to have built LLVM with -DLLVM_ENABLE_PROJECTS=mlir.

Be aware that the installed version of LLVM needs in general to match the committed version in externals/llvm-project. Using a different version may or may not work.

Build commands

After either cmake run (in-tree/out-of-tree), use one of the following commands to build the project:

# Build just torch-mlir (not all of LLVM)
cmake --build build --target tools/torch-mlir/all

# Run unit tests.
cmake --build build --target check-torch-mlir

# Run Python regression tests.
cmake --build build --target check-torch-mlir-python

# Build everything (including LLVM if in-tree)
cmake --build build

Setup Python Environment to export the built Python packages

export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples

Jupyter

Jupyter notebook:

python -m ipykernel install --user --name=torch-mlir --env PYTHONPATH "$PYTHONPATH"
# Open in jupyter, and then navigate to
# `examples/resnet_inference.ipynb` and use the `torch-mlir` kernel to run.
jupyter notebook

Example IR for a simple 1 layer MLP to show the compilation steps from TorchScript.

Interactive Use

The build_tools/write_env_file.sh script will output a .env file in the workspace folder with the correct PYTHONPATH set. This allows tools like VSCode to work by default for debugging. This file can also be manually source'd in a shell.

Bazel Build

NOTE Our Bazel build follows LLVM's Bazel build policy: only the subcommunity interested in Bazel is responsible for fixing it. Average Torch-MLIR developers should not be notified of any Bazel build issues and are not responsible for fixing any breakages (though any help is, of course, welcome). For more info, see LLVM's Peripheral Support Tier definition.

Torch-MLIR can also be built using Bazel (apart from the official CMake build) for users that depend on Bazel in their workflows. To build torch-mlir-opt using Bazel, follow these steps:

  1. Install Bazel if you don't already have it
  2. Install a relatively new release of Clang
  3. Build:
cd utils/bazel
bazel build @torch-mlir//...
  1. Find the built binary at bazel-bin/external/torch-mlir/torch-mlir-opt.

Testing

Torch-MLIR has two types of tests:

  1. End-to-end execution tests. These compile and run a program and check the result against the expected output from execution on native Torch. These use a homegrown testing framework (see python/torch_mlir_e2e_test/torchscript/framework.py) and the test suite lives at python/torch_mlir_e2e_test/test_suite/__init__.py.

  2. Compiler and Python API unit tests. These use LLVM's lit testing framework. For example, these might involve using torch-mlir-opt to run a pass and check the output with FileCheck.

Running execution (end-to-end) tests:

# Run all tests on the reference backend
./tools/torchscript_e2e_test.sh
# Run tests that match the regex `Conv2d`, with verbose errors.
./tools/torchscript_e2e_test.sh --filter Conv2d --verbose
# Run tests on the TOSA backend.
./tools/torchscript_e2e_test.sh --config tosa

Running unit tests.

To run all of the unit tests, run:

ninja check-torch-mlir-all

This can be broken down into

ninja check-torch-mlir check-torch-mlir-dialects check-torch-mlir-python

To run more fine-grained tests, you can do, for check-torch-mlir:

cd $TORCH_MLIR_BUILD_DIR/tools/torch-mlir/test
$TORCH_MLIR_BUILD_DIR/bin/llvm-lit $TORCH_MLIR_SRC_ROOT/test -v --filter=canonicalize

See the lit documentation for details on the available lit args.

For example, if you wanted to test just test/Dialect/Torch/canonicalize.mlir, then you might do

cd $TORCH_MLIR_BUILD_DIR/tools/torch-mlir/test
$TORCH_MLIR_BUILD_DIR/bin/llvm-lit $TORCH_MLIR_SRC_ROOT/test -v --filter=canonicalize.mlir

Most of the unit tests use the FileCheck tool to verify expected outputs.

Updating the LLVM submodule

Torch-MLIR maintains llvm-project (which contains, among other things, upstream MLIR) as a submodule in externals/llvm-project. We aim to update this at least weekly to new LLVM revisions to bring in the latest features and spread out over time the effort of updating our code for MLIR API breakages.

Updating the LLVM submodule is done by:

  1. In the externals/llvm-project directory, run git pull to update to the upstream revision of interest (such as a particular upstream change that is needed for your Torch-MLIR PR).
  2. Rebuild and test Torch-MLIR (see above), fixing any issues that arise. This might involve fixing various API breakages introduced upstream (they are likely unrelated to what you are working on). If these fixes are too complex, please file a work-in-progress PR explaining the issues you are running into asking for help so that someone from the community can help.
  3. Run build_tools/update_shape_lib.sh to update the shape library -- this is sometimes needed because upstream changes can affect canonicalization and other minor details of the IR in the shape library. See docs/shape_lib.md for more details on the shape library.

Here are some examples of PR's updating the LLVM submodule:

Other docs