This enables building Pytorch from source in the CI.
The build should mostly hit the ccache.
Release builds will follow once we have some runtime on the CI.
Remove all the libtorch downloads. If the user sets
-DTORCH_MLIR_USE_INSTALLED_PYTORCH=OFF then just build from src.
Doesn't change developer workflow since we still default to local
PyTorch versions.
TEST: Build and verify all tests (except one xfail quant) pass on linux
Found while trying to build torch-mlir on an AArch64 Linux VM, worth
a belts and braces to prevent such cases.
Change-Id: I89c6fccb62e666dbda0d9acac2d0ee43c2899e9b
On my local machine, `unzip` didn't exist (producing a "command not
found" error), but CMake ignored the error. Although the build did
succeed (because it found a previously-built version of libtorch), it
seems better to abort builds on such failures, so this patch checks the
return code of all external process invocations.
Along similar lines, this patch also updates the shell scripts in
`build_tools` to extensively use double-quoting to prevent unintentional
word splitting or globbing. Since some of the scripts execute `rm`
while using shell variables, this patch also adds the preamble `set -u`
to abort execution if an undefined variable is referenced, so that we
reduce the chances of executing `rm -rf /` if the path expression
happens to refer to an undefined variable.
Add an option to cache libtorch/ releases if you don't want to
download the latest. Add an option to enable source builds.
TESTS:
macOS: verify with / without cache downloads
verify source builds -- shared and static
Linux: Build Tests and Release builds
The MacOS builders are having linking trouble with the extension library.
Until it's fixed, all support for op extensions is disabled. It should be
easy to restore once the issue is resolved.
PyTorch allows new operators to be registered dynamically in modules.
Torch-mlir already makes it fairly straightforward to add support for
new operators, and this commit just extends that support to allow new
PyTorch ops to come from a external module.
This does *not* allow ops to be dynamically loaded into torch-mlir.
Torch-mlir must still be compiled with support built-in.
Add a `_torch_mlir_custom_op_example` subpackage to `torch_mlir` which
registers an demonstration op. It will not be imported by default when
importing torch_mlir. It's strictly for testing and documentation.
Adds an end-to-end test for the `torch_mlir_custom_op_example::identity` op.
With all these changes, we should now be actively testing PyTorch extension
support with all future patches.
1. With the help of `make_fx` we are able to get the full training graph
with weight updates.
2. NeuralNet_training passes. Bert_training passes after cherry-picking
https://github.com/llvm/torch-mlir/pull/844.
3. TODO: Remove the functorch's dependency after make_fx moves to
pytorch core.
* 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.
The `assemblyFormat` stuff (which generates unrolled, per-op C++ code)
was taking up a lot of compile time, and all the ops are essentially
printed with the same logic. So this PR makes them all call the same
helper function. This is done by using
`let hasCustomAssemblyFormat = 1` and then implementing `FooOp::parse`
and `FooOp::print`.
Additionally, the `Generated*Ops.td` files are all collapsed into just
`GeneratedTorchOps.td` (there is no reason to have the files separate,
since the files are very large anyway so one is always having to search
within them -- editors don't care that the file to search is now a bit
bigger :) ).
This reduces TorchOpsODSGenerated.cpp compile time (which is now
GeneratedTorchOps.cpp) from 39 to 31 seconds on my machine. This is
actually less than I expected, but this PR is an overall cleanup to the
code anyway. The next step will be to introduce (better) functionality
upstream for sharding the TorchOps.cpp.inc file, so that we can truly
parallelize the O(#ops) costs. This is also necessary, because after
this PR, TorchDialect.cpp is now the slowest file to compile, due to the
`addOperations<... all the ops ...>` call, which needs to be shareded
too.
This was an aspirational goal at an earlier stage in the project where
the focus was heavily on programs with state, control flow, and
lists/dicts. We will circle back to such programs likely 2022H2 at some
point, but for now, having this test doesn't add much, since basically
nothing works or is being worked on.
See the documentation in `docs/shape_lib.md` and
`docs/adding_a_shape_function.md` for an overview of the system.
This completely overhauls how we represent shape functions. In
particular, RefineTypes does not infer shapes anymore (only dtypes).
Shape functions are now written in (TorchScript'able) Python.
Recommended review order:
1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`.
1. Code and tests for ReifyShapeCalculations, DropShapeCalculations.
1. Code and tests for SimplifyShapeCalculations.
1. shape_lib_gen.py
1. Code and tests for new RefineTypes pass.
1. Random folders/canonicalizers in TorchOps.cpp and associated test in
`canonicalize.mlir`.
1. New ReadOnly trait inferred from the registry.
1. Any miscellaneous remaining stuff.
Example `-print-ir-after-all` for ElementwiseUnaryModule:
[IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212).
Example `-print-ir-after-all` for ElementwiseBinaryModule:
[IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
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.
This commit (with approval from all contributors) dual licenses
the torch-mlir project under both the standard LLVM license and the
standard PyTorch license. This will facilitate moving code between
torch-mlir and the two upstream projects.
The standard file comment is now:
```
// This file is licensed 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.
```
See `LICENSE` in the project root for the terms of both licenses.
This leaves no real code outside torch-mlir.
This also renames the "npcomp backend contract" to "linalg on tensors
backend contract" as the name of the abstraction layer that RefBackend
(IREE too) accepts.
A few remain in examples/docs that will be naturally be updated in due
time.
This regresses the list support and the general direction of more widely
supported control flow, lists/dicts/globals that we were going for with
the TorchScript path. The idea is that we are deferring that work to
make torch-mlir a very clean standalone thing. We will reboot it,
probably using some of the tools of iree_pydm to make it simpler, and in
a more natural place (such as an iree-torch repo that depends on IREE and
torch-mlir to build a working PyTorch frontend solution for IREE -- it
was really weird that npcomp depended on IREE).
powderluv
Ramana Radhakrishnan
Maksim Levental
Ashay Rane
Bob Adolf
Prashant Kumar
Vivek Khandelwal
Sean Silva
powderluv
Ahmed S. Taei
Yi Zhang