The ``dataclasses`` package makes sense for Python 3.6, becauses
``dataclasses`` is only included in the standard library with 3.7
version. Now, 3.6 has reached EOL, so all current supported versions of
Python (3.8, 3.9, 3.10, 3.11, 3.12) have this feature in their standard
libraries.
Therefore there's no need to install the ``dataclasses`` package now.
2.9.0 was released on December 28, 2021, and some following changes
require at least this version.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D150247
Some Ubuntu 20.04 images come with PyYAML 5.3.1 pre-installed through distutils. This makes pip very angry. See https://github.com/yaml/pyyaml/issues/349.
Since older versions of PyYAML should work for mlir, relax the version requirement to ease developer setup.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D143523
This change is pinning the requirements to a specific version (or a range) depending on the requirement. A couple of considerations:
* numpy 1.24 deprecates np.object, np.bool, np.float, np.complex, np.str, and np.int which are used heavily in onnx-mlir
* not all versions of each package are available on every platform - to the best of my knowledge, these ranges should work on Ubuntu, CentOS and Windows
Adding a minimum and maximum version, or pinning to a specific versions where possible, helps with two major goals - security and maintainability. It gives us an opportunity to make sure that the packages being used are not part of a security attack as well as guaranteeing that they support the features that mlir depends on (see note about numpy deprecation).
Let me know if you are aware of better versions or ranges to pin to.
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D142563
Add TACO tests to test/Integration/Dialect/SparseTensor/taco. Add the MLIR
PyTACO implementation as tools under the directory.
Reviewed By: aartbik, mehdi_amini
Differential Revision: https://reviews.llvm.org/D117260
The constructor function was being defined without indicating its "__init__"
name, which made it interpret it as a regular fuction rather than a
constructor. When overload resolution failed, Pybind would attempt to print the
arguments actually passed to the function, including "self", which is not
initialized since the constructor couldn't be called. This would result in
"__repr__" being called with "self" referencing an uninitialized MLIR C API
object, which in turn would cause undefined behavior when attempting to print
in C++. Even if the correct name is provided, the mechanism used by
PybindAdaptors.h to bind constructors directly as "__init__" functions taking
"self" is deprecated by Pybind. The new mechanism does not seem to have access
to a fully-constructed "self" object (i.e., the constructor in C++ takes a
`pybind11::detail::value_and_holder` that cannot be forwarded back to Python).
Instead, redefine "__new__" to perform the required checks (there are no
additional initialization needed for attributes and types as they are all
wrappers around a C++ pointer). "__new__" can call its equivalent on a
superclass without needing "self".
Bump pybind11 dependency to 3.8.0, which is the first version that allows one
to redefine "__new__".
Reviewed By: stellaraccident
Differential Revision: https://reviews.llvm.org/D117646
* NFC but has some fixes for CMake glitches discovered along the way (things not cleaning properly, co-mingled depends).
* Includes previously unsubmitted fix in D98681 and a TODO to fix it more appropriately in a smaller followup.
Differential Revision: https://reviews.llvm.org/D101493
