This commit fixes the aten.mean and aten.mean.dim op decomposition
for supporting large-sized inputs.
This commit also fixes the formatting for the file stats.py
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
The purpose of the test suite is to accelerate the development of the
compiler. However, we had various tests there that were not expected to
work, had no in-progress work being tested by the test, and nobody was
actively working on them. Having such tests in our test suite just adds
clutter and slows down development on the compiler.
-- aten.upsample_nearest2d.vec op is not present
owing to https://github.com/pytorch/pytorch/pull/85638
-- So this commit adds a lowering on aten.upsample_nearest2d.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
This commit renames the patterns used to match on lists of constant
values to `m_TorchListOfConstant{valueType}s`. This is needed to avoid
ambiguity for when `valueType` has `Optional` in it. In particular, it
makes it clear whether the values in the list are optional or the list
itself is optional.
Bazel LIT test support was added in https://github.com/llvm/torch-mlir/pull/1585. This PR enables the tests in CI.
```
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Executed [59](https://github.com/sjain-stanford/torch-mlir/actions/runs/3476816449/jobs/5812368489#step:7:60) out of 59 tests: 59 tests pass.
```
GHA workflow: https://github.com/sjain-stanford/torch-mlir/actions/runs/3476816449/jobs/5812368489
Adds support to run `.mlir` LIT tests in bazel.
```
bazel test @torch-mlir//test/...
```
Follow-on PR will contain these updates:
- Add tests to GHA CI workflow
- Add `.py` LIT tests to bazel
We currently pin the `torch` package to the latest nightly version, but
since `torchvision` depends on the `torch` package, the pip resolver
then has to run through an extensive list of `torchvision` packages that
can be installed with the pinned `torch` package. This search fails in
the RollPyTorch action, causing pip to settle on an old version of
`torchvision` that does not work with our tests. In reality, we are
only interested in a specific version of the `torchvision` package.
To make the dependency explicit and to prevent test failures because of
incorrect package installations, this patch makes two key changes:
1. `torchvision` is now pinned to the latest nightly release in
pytorch-requirements.txt along with the version of `torch` that is
necessary to install the requested `torchvision` package
2. The RollPyTorch action now looks for the latest `torchvision` package
instead of the latest `torch` package before writing the version
numbers for pinning in pytorch-requirements.txt
We want each build to be reproducible regardless of prior builds and
prior package installations, but pip, by default, uses cached packages
from previous invocations of `pip install`. As a result, the incorrect
dependencies downloaded in the RollPyTorch workflow in the main
repository cannot be reproduced in private forks of the repository. To
resolve this problem, this patch adds a `--no-cache-dir` flag to pip, so
that it fetches and inspects each requested package independent or prior
installations.
For AoT deployments models often have multiple exported methods.
This patch enables something like this:
```
class TwoMethodsModule(torch.nn.Module):
def sin(self, x):
return torch.ops.aten.sin(x)
def cos(self, x):
return torch.ops.aten.cos(x)
example_args = torch_mlir.ExampleArgs()
example_args.add_method("sin", torch.ones(2, 3))
example_args.add_method("cos", torch.ones(2, 4))
print(torch_mlir.compile(TwoMethodsModule(), example_args))
```
In the
[long-term](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md#tools-for-advanced-aot-deployments)
we will need to reconcile this with our story for stateful models and the
backend contract being purely functional. For now, this provides some basic
infra that seems harmless. Arguably, we could tighten up the backend contract
even more to only allow a single compiled function which would prohibit this or
require building out a layer above.
Fixes#1557
Now that the ninja pip package issue has been resolved, this patch
removes the pinned version from requirements.txt so that we can go back
to using the most recent version of ninja.
Going from ninja v1.10.2 to v1.11.1, there is a change that breaks the
CI builds with the following error:
```
CMake Error at CMakeLists.txt:47 (project):
Running
'/main_checkout/torch-mlir/docker_venv/bin/ninja' '--version'
failed with:
CMake Error: CMAKE_ASM_COMPILER not set, after EnableLanguage
```
Ostensibly, the reason for the error about the ASM compiler is because
llvm-project/llvm/CMakeLists.txt includes ASM among the list of
languages used in the LLVM project. Adding `-DCMAKE_ASM_COMPILER=clang`
does not resolve the error.
Until we figure out why the new version of ninja causes the build
failures, this patch pins the ninja to the one that worked.
Unless requested otherwise, PyTorch no longer installs most of the
header files under the caffe2 directory (see
https://github.com/pytorch/pytorch/pull/87986). This breaks our
importer code since we need to use the `MakeGuard()` function to execute
statements in the event of exceptions.
To fix this issue, this patch implements a rudimentary version of
PyTorch's ScopeGuard, where once the class variable goes out of scope,
it executes a predefined method.
Tanyo Kwok
Vivek Khandelwal
Maksim Levental
Sean Silva
Chi_Liu
Abhishek Varma
Kazuaki Ishizaki
Gaurav Shukla
Ramiro Leal-Cavazos
Sambhav Jain
AmosLewis
George Petterson
Roll PyTorch Action
Ashay Rane
Gleb Kazantaev
Daniel Ellis
Xiafei Qiu
Yuanqiang Liu
Jae Hoon (Antonio) Kim