torch-mlir/frontends/pytorch/setup.py

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# Build/install the npcomp-torch package.
# This uses PyTorch's setuptools support and requires an existing installation
# of npcomp-core in order to access its headers/libraries.
from pathlib import Path
from setuptools import find_packages, setup, Extension
from torch.utils import cpp_extension
try:
from npcomp import build as npcomp_build
except ModuleNotFoundError:
raise ModuleNotFoundError(
f"Could not import npcomp.build "
f"(do you have the npcomp-core package installed)")
# Get our sources.
this_dir = Path(__file__).parent
extension_sources = [str(p) for p in this_dir.joinpath("csrc").rglob("*.cpp")]
# Npcomp bits.
include_dirs = npcomp_build.get_include_dirs()
lib_dirs = npcomp_build.get_lib_dirs()
npcomp_libs = [npcomp_build.get_capi_link_library_name()]
setup(
name="npcomp-torch",
ext_modules=[
cpp_extension.CppExtension(
name="_torch_mlir",
sources=extension_sources,
include_dirs=include_dirs,
library_dirs=lib_dirs,
libraries=npcomp_libs),
],
cmdclass={
"build_ext": cpp_extension.BuildExtension
},
package_dir={
"": "./python",
},
packages=find_packages("./python", include=[
Add E2E support for tests with heavy dependencies (heavydep tests). The tests use the same (pure-Python) test framework as the normal torchscript_e2e_test.sh, but the tests are added in `build_tools/torchscript_e2e_heavydep_tests` instead of `frontends/pytorch/e2e_testing/torchscript`. Any needed dependencies can easily be configured in generate_serialized_tests.sh. We add an initial machine translation model with a complex set of dependencies to seed the curriculum there. I verified that this model gets to the point of MLIR import (it fails there with a segfault due to not being able to import the "Any" type). This required moving a few files from the `torch_mlir` Python module into multiple modules to isolate the code that depends on our C++ extensions (which now live in `torch_mlir` and `torch_mlir_torchscript_e2e_test_configs`) from the pure Python code (which now lives in `torch_mlir_torchscript`). This is an entirely mechanical change, and lots of imports needed to be updated. The dependency graph is: ``` torch_mlir_torchscript_e2e_test_configs / | / | / | V V torch_mlir_torchscript torch_mlir ``` The `torch_mlir_torchscript_e2e_test_configs` are then dependency-injected into the `torch_mlir_torchscript` modules to successfully assemble a working test harness (the code was already structured this way, but this new file organization allows the isolation from C++ code to actually happen). This isolation is critical to allowing the serialized programs to be transported across PyTorch versions and for the test harness to be used seamlessly to generate the heavydep tests. Also: - Extend `_Tracer` class to support nested property (submodule) accesses. Recommended review order: - "user-level" docs in README.md - code in `build_tools/torchscript_e2e_heavydep_tests`. - changes in `torch_mlir_torchscript/e2e_test/framework.py` - misc mechanical changes.
2021-07-10 03:22:45 +08:00
"torch_mlir",
"torch_mlir.*",
"torch_mlir_torchscript",
"torch_mlir_torchscript.*",
"torch_mlir_torchscript_e2e_test_configs",
"torch_mlir_torchscript_e2e_test_configs.*",
]),
)