mirror of https://github.com/llvm/torch-mlir
Move development notes to development.md (#800)
* Update README.md * Create development.md Add a separate development.md filepull/801/head
powderluv
GitHub
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50297864df
commit
a9ef4bb378
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README.md
134
README.md
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@ -33,7 +33,7 @@ We have few paths to lower down to the Torch MLIR Dialect.
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- Github issues [here](https://github.com/llvm/torch-mlir/issues)
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- [`torch-mlir` section](https://llvm.discourse.group/c/projects-that-want-to-become-official-llvm-projects/torch-mlir/41) of LLVM Discourse
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## Use pre-built snapshots of torch-mlir
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## Install torch-mlir snapshot
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This installs a pre-built snapshot of torch-mlir for Python 3.7/3.8/3.9/3.10 on Linux and macOS
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@ -68,119 +68,6 @@ torch-mlir prediction
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[('Labrador retriever', 70.66320037841797), ('golden retriever', 4.956601619720459), ('Chesapeake Bay retriever', 4.195651531219482)]
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```
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# Checkout and build from source
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## Check out the code
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```shell
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git clone https://github.com/llvm/torch-mlir
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cd torch-mlir
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git submodule update --init
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```
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## Setup your Python VirtualEnvironment and Dependencies
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```shell
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python -m venv mlir_venv
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source mlir_venv/bin/activate
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# Some older pip installs may not be able to handle the recent PyTorch deps
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python -m pip install --upgrade pip
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# Install latest PyTorch nightlies and build requirements.
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python -m pip install -r requirements.txt
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```
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## Build
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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.
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### Building torch-mlir in-tree
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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.
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```shell
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cmake -GNinja -Bbuild \
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-DCMAKE_C_COMPILER=clang \
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-DCMAKE_CXX_COMPILER=clang++ \
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-DPython3_FIND_VIRTUALENV=ONLY \
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-DLLVM_ENABLE_PROJECTS=mlir \
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-DLLVM_EXTERNAL_PROJECTS="torch-mlir;torch-mlir-dialects" \
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-DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR=`pwd` \
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-DLLVM_EXTERNAL_TORCH_MLIR_DIALECTS_SOURCE_DIR=`pwd`/externals/llvm-external-projects/torch-mlir-dialects \
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-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
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-DLLVM_TARGETS_TO_BUILD=host \
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externals/llvm-project/llvm
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```
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The following additional quality of life flags can be used to reduce build time:
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* Enabling ccache:
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```shell
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-DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache
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```
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* Enabling LLD (links in seconds compared to minutes)
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```shell
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-DCMAKE_EXE_LINKER_FLAGS_INIT="-fuse-ld=lld" -DCMAKE_MODULE_LINKER_FLAGS_INIT="-fuse-ld=lld" -DCMAKE_SHARED_LINKER_FLAGS_INIT="-fuse-ld=lld"
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# Use --ld-path= instead of -fuse-ld=lld for clang > 13
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```
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### Building against a pre-built LLVM
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If you have built llvm-project separately in the directory `$LLVM_INSTALL_DIR`, you can also build the project *out-of-tree* using the following command as template:
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```shell
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cmake -GNinja -Bbuild \
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-DCMAKE_C_COMPILER=clang \
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-DCMAKE_CXX_COMPILER=clang++ \
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-DPython3_FIND_VIRTUALENV=ONLY \
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-DMLIR_DIR="$LLVM_INSTALL_DIR/lib/cmake/mlir/" \
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-DLLVM_DIR="$LLVM_INSTALL_DIR/lib/cmake/llvm/" \
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-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
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-DLLVM_TARGETS_TO_BUILD=host \
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.
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```
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The same QoL CMake flags can be used to enable ccache and lld. Be sure to have built LLVM with `-DLLVM_ENABLE_PROJECTS=mlir`.
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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.
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### Build commands
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After either cmake run (in-tree/out-of-tree), use one of the following commands to build the project:
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```shell
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# Build just torch-mlir (not all of LLVM)
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cmake --build build --target tools/torch-mlir/all
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# Run unit tests.
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cmake --build build --target check-torch-mlir
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# Run Python regression tests.
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cmake --build build --target check-torch-mlir-python
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# Build everything (including LLVM if in-tree)
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cmake --build build
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```
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## Setup Python Environment to export the built Python packages
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```shell
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export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples
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```
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## Running execution (end-to-end) tests:
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```shell
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# Run E2E TorchScript tests. These compile and run the TorchScript program
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# through torch-mlir with a simplified MLIR CPU backend we call RefBackend
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python -m e2e_testing.torchscript.main --filter Conv2d --verbose
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```
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[Example IR](https://gist.github.com/silvasean/e74780f8a8a449339aac05c51e8b0caa) for a simple 1 layer MLP to show the compilation steps from TorchScript.
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Jupyter notebook:
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```shell
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python -m ipykernel install --user --name=torch-mlir --env PYTHONPATH "$PYTHONPATH"
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# Open in jupyter, and then navigate to
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# `examples/resnet_inference.ipynb` and use the `torch-mlir` kernel to run.
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jupyter notebook
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```
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### LazyTensorCore
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The LazyTensorCore integration is still in progress, and is being built on the
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@ -203,20 +90,5 @@ The project follows the conventions of typical MLIR-based projects:
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* `tools` for `torch-mlir-opt` and such.
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* `python` top level directory for Python code
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## Interactive Use
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The `build_tools/write_env_file.sh` script will output a `.env`
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file in the workspace folder with the correct PYTHONPATH set. This allows
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tools like VSCode to work by default for debugging. This file can also be
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manually `source`'d in a shell.
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## Build Python Packages
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We have preliminary support for building Python packages. This can be done
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with the following commands:
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```
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python -m pip install --upgrade pip
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python -m pip install -r requirements.txt
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CMAKE_GENERATOR=Ninja python setup.py bdist_wheel
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```
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## Developers
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If you would like to develop and build torch-mlir from source please look at [Development Notes](development.md)
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@ -0,0 +1,132 @@
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# Checkout and build from source
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## Check out the code
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```shell
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git clone https://github.com/llvm/torch-mlir
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cd torch-mlir
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git submodule update --init
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```
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## Setup your Python VirtualEnvironment and Dependencies
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```shell
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python -m venv mlir_venv
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source mlir_venv/bin/activate
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# Some older pip installs may not be able to handle the recent PyTorch deps
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python -m pip install --upgrade pip
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# Install latest PyTorch nightlies and build requirements.
|
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python -m pip install -r requirements.txt
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```
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## Build Python Packages
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We have preliminary support for building Python packages. This can be done
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with the following commands:
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```
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python -m pip install --upgrade pip
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python -m pip install -r requirements.txt
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CMAKE_GENERATOR=Ninja python setup.py bdist_wheel
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```
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## CMake Build
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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.
|
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|
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### Building torch-mlir in-tree
|
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|
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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.
|
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|
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```shell
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cmake -GNinja -Bbuild \
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-DCMAKE_C_COMPILER=clang \
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-DCMAKE_CXX_COMPILER=clang++ \
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-DPython3_FIND_VIRTUALENV=ONLY \
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-DLLVM_ENABLE_PROJECTS=mlir \
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-DLLVM_EXTERNAL_PROJECTS="torch-mlir;torch-mlir-dialects" \
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-DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR=`pwd` \
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-DLLVM_EXTERNAL_TORCH_MLIR_DIALECTS_SOURCE_DIR=`pwd`/externals/llvm-external-projects/torch-mlir-dialects \
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-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
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-DLLVM_TARGETS_TO_BUILD=host \
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externals/llvm-project/llvm
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```
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The following additional quality of life flags can be used to reduce build time:
|
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* Enabling ccache:
|
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```shell
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-DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache
|
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```
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* Enabling LLD (links in seconds compared to minutes)
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```shell
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-DCMAKE_EXE_LINKER_FLAGS_INIT="-fuse-ld=lld" -DCMAKE_MODULE_LINKER_FLAGS_INIT="-fuse-ld=lld" -DCMAKE_SHARED_LINKER_FLAGS_INIT="-fuse-ld=lld"
|
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# Use --ld-path= instead of -fuse-ld=lld for clang > 13
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```
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|
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### Building against a pre-built LLVM
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|
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If you have built llvm-project separately in the directory `$LLVM_INSTALL_DIR`, you can also build the project *out-of-tree* using the following command as template:
|
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```shell
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cmake -GNinja -Bbuild \
|
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-DCMAKE_C_COMPILER=clang \
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-DCMAKE_CXX_COMPILER=clang++ \
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-DPython3_FIND_VIRTUALENV=ONLY \
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-DMLIR_DIR="$LLVM_INSTALL_DIR/lib/cmake/mlir/" \
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-DLLVM_DIR="$LLVM_INSTALL_DIR/lib/cmake/llvm/" \
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-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
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-DLLVM_TARGETS_TO_BUILD=host \
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.
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```
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The same QoL CMake flags can be used to enable ccache and lld. Be sure to have built LLVM with `-DLLVM_ENABLE_PROJECTS=mlir`.
|
||||
|
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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.
|
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|
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|
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### Build commands
|
||||
|
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After either cmake run (in-tree/out-of-tree), use one of the following commands to build the project:
|
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```shell
|
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# Build just torch-mlir (not all of LLVM)
|
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cmake --build build --target tools/torch-mlir/all
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|
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# Run unit tests.
|
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cmake --build build --target check-torch-mlir
|
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|
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# Run Python regression tests.
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cmake --build build --target check-torch-mlir-python
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# Build everything (including LLVM if in-tree)
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cmake --build build
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```
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## Setup Python Environment to export the built Python packages
|
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```shell
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export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples
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```
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## Running execution (end-to-end) tests:
|
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|
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```shell
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# Run E2E TorchScript tests. These compile and run the TorchScript program
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# through torch-mlir with a simplified MLIR CPU backend we call RefBackend
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python -m e2e_testing.torchscript.main --filter Conv2d --verbose
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```
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||||
|
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[Example IR](https://gist.github.com/silvasean/e74780f8a8a449339aac05c51e8b0caa) for a simple 1 layer MLP to show the compilation steps from TorchScript.
|
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|
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Jupyter notebook:
|
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```shell
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python -m ipykernel install --user --name=torch-mlir --env PYTHONPATH "$PYTHONPATH"
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# Open in jupyter, and then navigate to
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# `examples/resnet_inference.ipynb` and use the `torch-mlir` kernel to run.
|
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jupyter notebook
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```
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||||
|
||||
|
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## 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.
|
||||
|
||||
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