* Propagate device data names
* Address PR comment
* Add example usage
* Add test for device data names
* Make TorchMlirComputation fields protected
* Add lazy backend device data name unit tests
* Disable lazy backend tests if LTC is disabled
* Add comments
* 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>
This avoids issues where PyTorch version drift has made things
incompatible.
One caveat is that you will need to specify
`-f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
--pre` on the command line for pip to know where to find the nightly
packages (there is no way around this) -- this is easiest to do by
simultaneously passing `-r requirements.txt` on the pip command line.
I am investigating the breakage.
Also, fix "externals" rename in setup.py and some cases where we weren't
using `requirements.txt` consistently.
Also, fix a case where the packaging script would get confused due to
".." in the path name.
This is intended to explore support for non-structured ops that can't
be modeled by Linalg dialect. `tm_tensor.scan` and `tm_tensor.scatter`
are added as the first such ops. The dialect should aim to be
upstreamed in the future.
* Also adds a requirements.txt and updates docs to reference it versus stringy pip install.
* Adds doc with instructions on creating a wheel.
Fixes#370
* Now the parts of the MLIR API are directly exported under the npcomp module (i.e. `npcomp.ir`, etc).
* Has required fixes for https://reviews.llvm.org/D108489
* Deletes npcomp.tracing vs fixing it because it was a very early experiment that will not be carried forward.
* This makes the npcomp python distribution completely standalone and separate from an mlir installation.
* Makes most of npcomp itself relocatable for future use as a library.
* Most things are a namespace package now. In the future we can s/torch_mlir/npcomp.frontends.torch/ and have it layer properly.
* Adds a minimal setup.py for frontends/pytorch
* Makes npcomp-core export its headers and libraries
* Adds a script to build packages.
* Adds CI step to package and smoke test.
* Will need some more tweaks and coordination prior to deploying (version locking etc).
Jae Hoon (Antonio) Kim
Ashay Rane
Henry Tu
powderluv
Ziheng Jiang
Sean Silva
Yi Zhang
Stella Laurenzo