# 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.
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:
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:
[Example IR](https://gist.github.com/silvasean/e74780f8a8a449339aac05c51e8b0caa) for a simple 1 layer MLP to show the compilation steps from TorchScript.
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](https://docs.bazel.build/versions/main/install.html) if you don't already have it
2. Install a relatively new release of [Clang](https://releases.llvm.org/download.html)
3. Build:
```shell
cd utils/bazel
bazel build @torch-mlir//...
```
4. Find the built binary at `bazel-bin/external/torch-mlir/torch-mlir-opt`.
# Unexpected test failures with PyTorch / Libtorch skew
Torch-MLIR currently by default links to libtorch binaries and tests are run with the PyTorch nightlies. This can cause version / api
skew in your tests like (https://github.com/llvm/torch-mlir/issues/1007). If you notice any unexpected test failures please follow the steps below:
```
rm -rf libtorch* # note the asterisk after libtorch, since there is also a .zip file that needs to be removed
rm -rf build/
python -m pip install -r requirements.txt --upgrade # to get the latest pytorch
# Then rebuild and test torch-mlir
```
We expect this to be fixed once we take on a dependency on PyTorch and build it from source. That work is being tracked in [this](https://github.com/llvm/torch-mlir/tree/release-src-build) branch.
- Of particular interest in the [How to add end-to-end support for new Torch ops](https://github.com/llvm/torch-mlir/wiki/Torch-ops-E2E-implementation) doc.
