torch-mlir/test/lit.cfg.py

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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
# See https://llvm.org/LICENSE.txt for license information.
# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
# Also available under a BSD-style license. See LICENSE.
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
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import os
import platform
import re
import subprocess
import tempfile
import lit.formats
import lit.util
from lit.llvm import llvm_config
from lit.llvm.subst import ToolSubst
from lit.llvm.subst import FindTool
# Configuration file for the 'lit' test runner.
# name: The name of this test suite.
config.name = 'TORCH_MLIR'
config.test_format = lit.formats.ShTest(not llvm_config.use_lit_shell)
# suffixes: A list of file extensions to treat as test files.
config.suffixes = ['.mlir', '.py']
# test_source_root: The root path where tests are located.
config.test_source_root = os.path.dirname(__file__)
# test_exec_root: The root path where tests should be run.
config.test_exec_root = os.path.join(config.torch_mlir_obj_root, 'test')
config.substitutions.append(('%PATH%', config.environment['PATH']))
config.substitutions.append(('%shlibext', config.llvm_shlib_ext))
llvm_config.with_system_environment(['HOME', 'INCLUDE', 'LIB', 'TMP', 'TEMP'])
#llvm_config.use_default_substitutions()
# excludes: A list of directories to exclude from the testsuite. The 'Inputs'
# subdirectories contain auxiliary inputs for various tests in their parent
# directories.
config.excludes = [
'Inputs', 'Examples', 'CMakeLists.txt', 'README.txt', 'LICENSE.txt',
'lit.cfg.py', 'lit.site.cfg.py'
]
# test_source_root: The root path where tests are located.
config.test_source_root = os.path.dirname(__file__)
# test_exec_root: The root path where tests should be run.
config.test_exec_root = os.path.join(config.torch_mlir_obj_root, 'test')
config.standalone_tools_dir = os.path.join(config.torch_mlir_obj_root, 'bin')
# Tweak the PATH to include the tools dir.
llvm_config.with_environment('PATH', config.llvm_tools_dir, append_path=True)
Miscellaneous fixes for Windows builds (#1376) * test: allow spaces in path to Python executable On Windows, the path to the Python binary may contain spaces, so this patch adds quotes around the path to the python executable. Thanks to @sstamenova for suggesting the fix! * python: remove header file that causes Windows build failures Similar to https://reviews.llvm.org/D125284, we can safely remove this header file without affecting the build on either Linux. It is necessary to remove this header file on Windows builds since otherwise it causes build errors. * python: drop `TORCH_API` from function defined in Torch-MLIR `TORCH_API` should apply to functions that are either exported by libtorch.so or ones that are imported from libtorch.so by its downstream consumers (like Torch-MLIR). Neither case applies to the `importJitFunctionAsFuncOp()` function, since it is defined in Torch-MLIR (and thus outside libtorch.so). This patch fixes the problem by dropping `TORCH_API` from that function's declaration. * python: make output of class anotations deterministic The `class-annotator-repr.py` test checks for class annotations in a specific order, but prior to this patch, the order was non-deterministic, since the code iterated on an _unordered_ map. This patch makes the iteration order deterministic through two changes: 1. using a sorted map 2. using the class qualified name instead of the address of the class in memory * test: use Python3_EXECUTABLE as interpreter path for consistency This ensures that tests use the Python3 version that was detected using CMake, instead of whichever python version that happens to be in the PATH variable when invoking the test. * test: fix RUN string The parenthesis syntax does not run on Windows (the shell interprets the `(` character as part of the path). Moreover, the ODR violation in the comment no longer seems to apply. * python: port parallel test framework to Windows Since Windows does not support `fork` natively, Python's `multiprocessing` module needs to use `spawn` on Windows. However, to use `spawn`, the multiprocessing module serializes (or pickles) the worker function and its arguments. Sadly, the multiprocessing module (both the default one in Python and the one that is extended in PyTorch) is unable to serialize lambda functions (see https://stackoverflow.com/a/19985580) for detals. Unfortunately, given how our tests are structured, we require that the function under test is passed as an argument to another function, so we cannot sidestep our use of lambda functions. To resolve this problem, this patch makes use of the `multiprocess` and `dill` Python modules, which together offers a multiprocessing mechanism that can serialize lambda functions. The multiprocess module also offers a process pool, which simplifies the code for our parallel testing framework.
2022-09-30 01:07:43 +08:00
# On Windows the path to python could contains spaces in which case it needs to
# be provided in quotes. This is the equivalent of how %python is setup in
# llvm/utils/lit/lit/llvm/config.py.
if "Windows" in config.host_os:
config.python_executable = '"%s"' % (config.python_executable)
tool_dirs = [config.standalone_tools_dir, config.llvm_tools_dir, config.torch_mlir_obj_root]
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
tools = [
'torch-mlir-opt',
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
2021-09-10 03:24:10 +08:00
ToolSubst('%PYTHON', config.python_executable, unresolved='ignore'),
]
llvm_config.add_tool_substitutions(tools, tool_dirs)
if config.enable_bindings_python:
llvm_config.with_environment('PYTHONPATH', [
os.path.join(config.torch_mlir_python_packages_dir, 'torch_mlir'),
[torch-mlir earthmoving (1/N)] C/C++ code movement. This creates the `external/torch-mlir` directory as an LLVM_EXTERNAL_PROJECTS-compatible project (analogous to `iree-dialects`) and completes movement/rename of all pure MLIR C/C++ compiler code into there. The next step will be to move all the Python code / code that links/includes PyTorch C++ code (which currently lives in `frontends/pytorch`) into a subdirectory here. I call this "earthmoving" because it is mostly mechanical changes and renames. As a quick summary (we can change this down the road easily) - C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch` - CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet` - preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_` - CMake `NPCOMPFoo -> TorchMLIRFoo` The goal of this is to create a standalone project creating a center of mass for entry into the MLIR ecosystem from PyTorch, suitable in scope for eventual inclusion/ownership in PyTorch. The idea is that `external/torch-mlir` will some day be pulled out into its own repository, and then npcomp will simply pull it in as a submodule. Layering-wise, what lives in `torch-mlir` lowers code from PyTorch (currently TorchScript, but TorchFX or pytorch/xla-style tracing are possible extensions) down to what we have been calling the "Torch backend contract" which is cleaned up IR (inlining, simplifcation, conversion to value tensors, ...) entirely in the `torch` dialect. This is the branching off point for further lowering, of which npcomp takes one opinion (outside `torch-mlir` of course!), namely the `TorchConversion` dialect/transforms which lower to IR suitable for IREE and other linalg-on-tensors based lower-level compilers. Summary of changes: - move `{include,lib,test}/Dialect/Torch` into `torch-mlir` - move relevant parts of CAPI into `torch-mlir`. - leave a few things related to the `torch-mlir` Python build commented out, which should be resolved in a subsequent change.
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],
append_path=True)