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@ -33,7 +33,7 @@ We have few paths to lower down to the Torch MLIR Dialect.
- Github issues [here](https://github.com/llvm/torch-mlir/issues) - 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 - [`torch-mlir` section](https://llvm.discourse.group/c/projects-that-want-to-become-official-llvm-projects/torch-mlir/41) of LLVM Discourse
## Use pre-built snapshots of torch-mlir ## Install torch-mlir snapshot
This installs a pre-built snapshot of torch-mlir for Python 3.7/3.8/3.9/3.10 on Linux and macOS This installs a pre-built snapshot of torch-mlir for Python 3.7/3.8/3.9/3.10 on Linux and macOS
@ -68,119 +68,6 @@ torch-mlir prediction
[('Labrador retriever', 70.66320037841797), ('golden retriever', 4.956601619720459), ('Chesapeake Bay retriever', 4.195651531219482)] [('Labrador retriever', 70.66320037841797), ('golden retriever', 4.956601619720459), ('Chesapeake Bay retriever', 4.195651531219482)]
``` ```
# Checkout and build from source
## Check out the code
```shell
git clone https://github.com/llvm/torch-mlir
cd torch-mlir
git submodule update --init
```
## Setup your Python VirtualEnvironment and Dependencies
```shell
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
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.
```shell
cmake -GNinja -Bbuild \
-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:
```shell
-DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache
```
* Enabling LLD (links in seconds compared to minutes)
```shell
-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 `$LLVM_INSTALL_DIR`, you can also build the project *out-of-tree* using the following command as template:
```shell
cmake -GNinja -Bbuild \
-DCMAKE_C_COMPILER=clang \
-DCMAKE_CXX_COMPILER=clang++ \
-DPython3_FIND_VIRTUALENV=ONLY \
-DMLIR_DIR="$LLVM_INSTALL_DIR/lib/cmake/mlir/" \
-DLLVM_DIR="$LLVM_INSTALL_DIR/lib/cmake/llvm/" \
-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
-DLLVM_TARGETS_TO_BUILD=host \
.
```
The same QoL CMake flags can be used to enable ccache and lld. Be sure to have built LLVM with `-DLLVM_ENABLE_PROJECTS=mlir`.
Be aware that the installed version of LLVM needs in general to match the committed version in `externals/llvm-project`. Using a different version may or may not work.
### Build commands
After either cmake run (in-tree/out-of-tree), use one of the following commands to build the project:
```shell
# Build just torch-mlir (not all of LLVM)
cmake --build build --target tools/torch-mlir/all
# Run unit tests.
cmake --build build --target check-torch-mlir
# Run Python regression tests.
cmake --build build --target check-torch-mlir-python
# Build everything (including LLVM if in-tree)
cmake --build build
```
## Setup Python Environment to export the built Python packages
```shell
export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples
```
## Running execution (end-to-end) tests:
```shell
# Run E2E TorchScript tests. These compile and run the TorchScript program
# through torch-mlir with a simplified MLIR CPU backend we call RefBackend
python -m e2e_testing.torchscript.main --filter Conv2d --verbose
```
[Example IR](https://gist.github.com/silvasean/e74780f8a8a449339aac05c51e8b0caa) for a simple 1 layer MLP to show the compilation steps from TorchScript.
Jupyter notebook:
```shell
python -m ipykernel install --user --name=torch-mlir --env PYTHONPATH "$PYTHONPATH"
# Open in jupyter, and then navigate to
# `examples/resnet_inference.ipynb` and use the `torch-mlir` kernel to run.
jupyter notebook
```
### LazyTensorCore ### LazyTensorCore
The LazyTensorCore integration is still in progress, and is being built on the The LazyTensorCore integration is still in progress, and is being built on the
@ -203,20 +90,5 @@ The project follows the conventions of typical MLIR-based projects:
* `tools` for `torch-mlir-opt` and such. * `tools` for `torch-mlir-opt` and such.
* `python` top level directory for Python code * `python` top level directory for Python code
## Interactive Use ## Developers
If you would like to develop and build torch-mlir from source please look at [Development Notes](development.md)
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.
## 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
```

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@ -0,0 +1,132 @@
# Checkout and build from source
## Check out the code
```shell
git clone https://github.com/llvm/torch-mlir
cd torch-mlir
git submodule update --init
```
## Setup your Python VirtualEnvironment and Dependencies
```shell
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.
```shell
cmake -GNinja -Bbuild \
-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:
```shell
-DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache
```
* Enabling LLD (links in seconds compared to minutes)
```shell
-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 `$LLVM_INSTALL_DIR`, you can also build the project *out-of-tree* using the following command as template:
```shell
cmake -GNinja -Bbuild \
-DCMAKE_C_COMPILER=clang \
-DCMAKE_CXX_COMPILER=clang++ \
-DPython3_FIND_VIRTUALENV=ONLY \
-DMLIR_DIR="$LLVM_INSTALL_DIR/lib/cmake/mlir/" \
-DLLVM_DIR="$LLVM_INSTALL_DIR/lib/cmake/llvm/" \
-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
-DLLVM_TARGETS_TO_BUILD=host \
.
```
The same QoL CMake flags can be used to enable ccache and lld. Be sure to have built LLVM with `-DLLVM_ENABLE_PROJECTS=mlir`.
Be aware that the installed version of LLVM needs in general to match the committed version in `externals/llvm-project`. Using a different version may or may not work.
### Build commands
After either cmake run (in-tree/out-of-tree), use one of the following commands to build the project:
```shell
# Build just torch-mlir (not all of LLVM)
cmake --build build --target tools/torch-mlir/all
# Run unit tests.
cmake --build build --target check-torch-mlir
# Run Python regression tests.
cmake --build build --target check-torch-mlir-python
# Build everything (including LLVM if in-tree)
cmake --build build
```
## Setup Python Environment to export the built Python packages
```shell
export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples
```
## Running execution (end-to-end) tests:
```shell
# Run E2E TorchScript tests. These compile and run the TorchScript program
# through torch-mlir with a simplified MLIR CPU backend we call RefBackend
python -m e2e_testing.torchscript.main --filter Conv2d --verbose
```
[Example IR](https://gist.github.com/silvasean/e74780f8a8a449339aac05c51e8b0caa) for a simple 1 layer MLP to show the compilation steps from TorchScript.
Jupyter notebook:
```shell
python -m ipykernel install --user --name=torch-mlir --env PYTHONPATH "$PYTHONPATH"
# Open in jupyter, and then navigate to
# `examples/resnet_inference.ipynb` and use the `torch-mlir` kernel to run.
jupyter notebook
```
## 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.