mirror of https://github.com/llvm/torch-mlir
Update out of date docs (#2602)
Some of docs referred to old file paths that no longer exists. This patch updates some of the instructions that I happened to notice were out of date. This is not a full updatepull/2579/merge
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@ -13,7 +13,7 @@ portable_realpath() {
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td="$(portable_realpath "$(dirname "$0")"/..)"
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build_dir="$(portable_realpath "${TORCH_MLIR_BUILD_DIR:-$td/build}")"
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python_packages_dir="$build_dir/tools/torch-mlir/python_packages"
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python_packages_dir="$build_dir/python_packages"
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write_env_file() {
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echo "Updating $build_dir/.env file"
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@ -5,7 +5,7 @@
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Adding support for a Torch operator in Torch-MLIR should always be accompanied
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by at least one end-to-end test to make sure the implementation of the op
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matches the behavior of PyTorch. The tests live in the
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`torch-mlir/python/torch_mlir_e2e_test/test_suite/` directory. When adding a new
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`torch-mlir/projects/pt1/python/torch_mlir_e2e_test/test_suite` directory. When adding a new
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test, choose a file that best matches the op you're testing, and if there is no
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file that best matches add a new file for your op.
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@ -20,6 +20,7 @@ source mlir_venv/bin/activate
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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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python -m pip install -r torchvision-requirements.txt
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```
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## CMake Build
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@ -108,25 +109,25 @@ cmake --build build
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### Linux and macOS
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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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export PYTHONPATH=`pwd`/build/python_packages/torch_mlir:`pwd`/projects/pt1/examples
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```
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### Windows PowerShell
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```shell
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$env:PYTHONPATH = "$PWD/build/tools/torch-mlir/python_packages/torch_mlir;$PWD/examples"
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$env:PYTHONPATH = "$PWD/build/python_packages/torch_mlir;$PWD/projects/pt1/examples"
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```
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## Testing MLIR output in various dialects
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To test the compiler's output to the different MLIR dialects, you can use the example `examples/torchscript_resnet18_all_output_types.py`.
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To test the compiler's output to the different MLIR dialects, you can use the example `projects/pt1/examples/torchscript_resnet18_all_output_types.py`.
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Make sure you have activated the virtualenv and set the `PYTHONPATH` above
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(if running on Windows, modify the environment variable as shown above):
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```shell
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source mlir_venv/bin/activate
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export PYTHONPATH=`pwd`/build/tools/torch-mlir/python_packages/torch_mlir:`pwd`/examples
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python examples/torchscript_resnet18_all_output_types.py
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export PYTHONPATH=`pwd`/build/tpython_packages/torch_mlir:`pwd`/projects/pt1/examples
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python projects/pt1/examples/torchscript_resnet18_all_output_types.py
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```
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This will display the Resnet18 network example in three dialects: TORCH, LINALG on TENSORS and TOSA.
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@ -331,8 +332,8 @@ Torch-MLIR has two types of tests:
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1. End-to-end execution tests. These compile and run a program and check the
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result against the expected output from execution on native Torch. These use
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a homegrown testing framework (see
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`python/torch_mlir_e2e_test/torchscript/framework.py`) and the test suite
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lives at `python/torch_mlir_e2e_test/test_suite/__init__.py`.
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`projects/pt1/python/torch_mlir_e2e_test/framework.py`) and the test suite
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lives at `projects/pt1/python/torch_mlir_e2e_test/test_suite/__init__.py`.
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2. Compiler and Python API unit tests. These use LLVM's `lit` testing framework.
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For example, these might involve using `torch-mlir-opt` to run a pass and
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