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torch-mlir/requirements.txt

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CI: miscellaneous fixes for Release builds (#1781) - Use v3 of actions/checkout, since the version we use (v2) uses Node.js 12, which is deprecated by GitHub. - Source the PowerShell venv sctipt (instead of the bash sript) since the calling script is a PowerShell script. Without this, the build doesn't use venv at all. - Make the build dependencies in whl-requirements.txt (used by setup.py) match those in requirements.txt. To that end, this patch creates a build-requirements.txt that is referenced by requirements.txt and whl-requirements.txt.
2023-01-07 10:41:43 +08:00
-r build-requirements.txt
WIP: No PyTorch dep (#1854)
2023-02-14 04:21:06 +08:00
-r pytorch-requirements.txt
Add PyYaml to requirements.txt (#1174) Building on a fresh environment + virtualenv + in-tree build errors out becayse PyYaml isn't installed. Adding to requirements.txt fixes that. Fixes #1173
2022-08-12 00:59:39 +08:00
Add oneshot release snapshot for test/ondemand (#768) * Add oneshot release snapshot for test/ondemand Add some build scripts to test new release flow based on IREE. Wont affect current builds, once this works well we can plumb it in. Build with manylinux docker * Fixes a few issues found when debugging powderluv's setup. * It is optional to link against Python3_LIBRARIES. Check that and don't do it if they don't exist for this config. * Clean and auditwheel need to operate on sanitized package names. So "torch_mlir" vs "torch-mlir". * Adds a pyproject.toml file that pins the build dependencies needed to detect both Torch and Python (the MLIR Python build was failing to detect because Numpy wasn't in the pip venv). * Commented out auditwheel: These wheels are not PyPi compliant since they weak link to libtorch at runtime. However, they should be fine to deploy to users. * Adds the --extra-index-url to the pip wheel command, allowing PyTorch to be found. * Hack setup.py to remove the _mlir_libs dir before building. This keeps back-to-back versions from accumulating in the wheels for subsequent versions. IREE has a more principled way of doing this, but what I have here should work. Co-authored-by: Stella Laurenzo <stellaraccident@gmail.com>
2022-04-21 17:19:12 +08:00
# Test Requirements
pillow
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
dill
multiprocess