mirror of https://github.com/llvm/torch-mlir
Updates to Readme.md (#334)
powderluv
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README.md
194
README.md
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@ -30,14 +30,87 @@ We have few paths to lower down to the Torch MLIR Dialect.
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This path provides the upcoming LTC path of capture. It is based of an unstable devel branch but is the closest way for you to adapt any existing torch_xla derivatives.
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- “ACAP” - Deprecated torch_xla based capture Mentioned here for completeness.
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## Examples
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There are few examples of lowering down via path from PyTorch to MLIR and using the “mlir-cpu-runner” to target a CPU backend. Obviously this is just a starting point and you can import this project into your larger MLIR projects to continue lowering to target GPUs and other Accelerators.
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## Check out the code
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## Project Communication
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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 Environment
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```
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python -m venv mlir_venv
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source mlir_venv/bin/activate
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python -m pip install --upgrade pip #Some older pip installs may not be able to handle the recent PyTorch deps
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# Install latest PyTorch nightlies
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python -m pip install --pre torch torchvision pybind11 -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
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```
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## Build
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```
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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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-DLLVM_ENABLE_PROJECTS=mlir \
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-DLLVM_EXTERNAL_PROJECTS=torch-mlir \
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-DLLVM_EXTERNAL_TORCH_MLIR_SOURCE_DIR=`pwd` \
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-DMLIR_ENABLE_BINDINGS_PYTHON=ON \
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-DLLVM_TARGETS_TO_BUILD=host \
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external/llvm-project/llvm
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# Additional quality of life CMake flags:
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# Enable ccache:
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# -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache
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# Enale LLD (links in seconds compared to minutes)
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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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cmake --build build
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```
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## Demos
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## Setup ENV
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```
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export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages
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```
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### TorchScript
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Running execution (end-to-end) tests:
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```
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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 linalg-on-tensors based backend we call
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# RefBackend (more production-grade backends at this same abstraction layer
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# exist in the MLIR community, such as IREE).
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./tools/torchscript_e2e_test.sh --filter Conv2d --verbose
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```
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Standalone script to generate and run a ResNet18 model:
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```
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# Run ResNet18 as a standalone script.
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python examples/torchscript_resnet18_e2e.py
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```
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Jupyter notebook:
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```
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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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### TorchFX
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TODO
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### Lazy Tensor Core
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TODO
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- `#torch-mlir` channel on the LLVM [Discord](https://discord.gg/xS7Z362)
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- issues/PR's on this github repo
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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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## Repository Layout
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@ -55,109 +128,8 @@ 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 Instructions
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## Project Communication
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```shell
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# From checkout directory.
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git submodule init
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git submodule update
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# Use clang and lld to build (optional but recommended).
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LLVM_VERSION=10
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export CC=clang-$LLVM_VERSION
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export CXX=clang++-$LLVM_VERSION
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export LDFLAGS=-fuse-ld=$(which ld.lld-$LLVM_VERSION)
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# Install PyTorch. We currently track and require the nighly build.
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# If a usable PyTorch package is installed, the default cmake settings will
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# enable the PyTorch frontend.
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pip3 install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
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# Invoke CMake and build. This will also run all unit tests.
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./build_tools/build_standalone.sh
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# Run write_env_file.sh to emit a .env file with needed
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# PYTHONPATH setup.
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./build_tools/write_env_file.sh
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source .env
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```
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## Demos
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### TorchScript
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A lot of the prior effort on torch-mlir has gone into the TorchScript compiler,
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with a path to execution via linalg-on-tensors (a commonly used representation
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of tensor computations in the MLIR community). Thus, this path has the most
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extensive testing and functionality.
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Running execution (end-to-end) tests:
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```
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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 linalg-on-tensors based backend we call
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# RefBackend (more production-grade backends at this same abstraction layer
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# exist in the MLIR community, such as IREE).
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./tools/torchscript_e2e_test.sh --filter Conv2d --verbose
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```
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Standalone script:
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```
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# Run ResNet18 as a standalone script.
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python examples/torchscript_resnet18_e2e.py
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```
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Jupyter notebook:
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```
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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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### TorchFX
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TODO
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### Lazy Tensor Core
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TODO
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### Additional TorchScript end-to-end tests with heavy dependencies
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Some of the Torchscript end-to-end tests require additional dependencies which
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don't make sense to include as part of the default torch-mlir setup.
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Additionally, these dependencies often don't work with the same HEAD PyTorch
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version that torch-mlir builds against at the C++ level (the TorchScript
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importer is written in C++)
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We have a self-contained script that generates all the needed artifacts from a
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self-contained virtual environment. It can be used like so:
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```shell
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# Build the virtual environment in the specified directory and generate the
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# serialized test artifacts in the other specified directory.
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# This command is safe to re-run if you have already built the virtual
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# environment and just changed the tests.
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build_tools/torchscript_e2e_heavydep_tests/generate_serialized_tests.sh \
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path/to/heavydep_venv \
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path/to/heavydep_serialized_tests
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# Add the --serialized-test-dir flag to point at the directory containing the
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# serialized tests. All other functionality is the same as the normal invocation
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# of torchscript_e2e_test.sh, but the serialized tests will be available.
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tools/torchscript_e2e_test.sh --serialized-test-dir=path/to/heavydep_serialized_tests
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```
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The tests use the same (pure-Python) test framework as the normal
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torchscript_e2e_test.sh, but the tests are added in
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`build_tools/torchscript_e2e_heavydep_tests` instead of
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`frontends/pytorch/e2e_testing/torchscript`.
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We rely critically on serialized TorchScript compatibility across PyTorch
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versions to transport the tests + pure-Python compatibility of the `torch`
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API, which has worked well so far.
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- `#torch-mlir` channel on the LLVM [Discord](https://discord.gg/xS7Z362)
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- 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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@ -0,0 +1,33 @@
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### Additional end-to-end tests with heavy dependencies (heavydep tests)
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Some end-to-end tests require additional dependencies which don't make sense to
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include as part of the default npcomp setup. Additionally, these dependencies
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often don't work with the same HEAD PyTorch version that npcomp builds against
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at the C++ level.
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We have a self-contained script that generates all the needed artifacts from a
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self-contained virtual environment. It can be used like so:
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```shell
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# Build the virtual environment in the specified directory and generate the
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# serialized test artifacts in the other specified directory.
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# This command is safe to re-run if you have already built the virtual
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# environment and just changed the tests.
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build_tools/torchscript_e2e_heavydep_tests/generate_serialized_tests.sh \
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path/to/heavydep_venv \
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path/to/heavydep_serialized_tests
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# Add the --serialized-test-dir flag to point at the directory containing the
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# serialized tests. All other functionality is the same as the normal invocation
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# of torchscript_e2e_test.sh, but the serialized tests will be available.
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tools/torchscript_e2e_test.sh --serialized-test-dir=path/to/heavydep_serialized_tests
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```
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The tests use the same (pure-Python) test framework as the normal
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torchscript_e2e_test.sh, but the tests are added in
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`build_tools/torchscript_e2e_heavydep_tests` instead of
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`frontends/pytorch/e2e_testing/torchscript`.
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We rely critically on serialized TorchScript compatibility across PyTorch
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versions to transport the tests + pure-Python compatibility of the `torch`
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API, which has worked well so far.
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