This PR does a number of things:
- Move linalg.vector_norm to structured kernels and simplify the logic
- Fixes a number of prexisting issues with the dtype kwarg of these ops
- Heavily simplifies and corrects the logic of `linalg.matrix_norm` and `linalg.norm` to be consistent with the docs
- Before the `_out` versions of these functions were incorrect
- Their implementation is now as efficient as expected, as it avoids reimplementing these operations whenever possible.
- Deprecates `torch.frobenius_norm` and `torch.nuclear_norm`, as they were exposed in the API and they are apparently being used in mobile (??!!) even though they were not documented and their implementation was slow.
- I'd love to get rid of these functions already, but I guess we have to go through their deprecation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76547
Approved by: https://github.com/mruberry
We don't have any coverage for meta tensor correctness for backwards
because torch function mode can only allow us to interpose on
Python torch API calls, but backwards invocations happen from C++.
To make this possible, I add torch_dispatch_meta test which runs the
tests with __torch_dispatch__
While doing this, I needed to generate fresh expected failure / skip
lists for the new test suite, and I discovered that my original
scaffolding for this purpose was woefully insufficient. So I rewrote
how the test framework worked, and at the same time rewrote the
__torch_function__ code to also use the new logic. Here's whats
new:
- Expected failure / skip is now done on a per function call basis,
rather than the entire test. This means that separate OpInfo
samples for a function don't affect each other.
- There are now only two lists: expect failure list (where the test
consistently fails on all runs) and skip list (where the test
sometimes passes and fails.
- We explicitly notate the dtype that failed. I considered detecting
when something failed on all dtypes, but this was complicated and
listing everything out seemed to be nice and simple. To keep the
dtypes short, I introduce a shorthand notation for dtypes.
- Conversion to meta tensors is factored into its own class
MetaConverter
- To regenerate the expected failure / skip lists, just run with
PYTORCH_COLLECT_EXPECT and filter on a specific test type
(test_meta or test_dispatch_meta) for whichever you want to update.
Other misc fixes:
- Fix max_pool1d to work with BFloat16 in all circumstances, by making
it dispatch and then fixing a minor compile error (constexpr doesn't
work with BFloat16)
- Add resolve_name for turning random torch API functions into string
names
- Add push classmethod to the Mode classes, so that you can more easily
push a mode onto the mode stack
- Add some more skips for missing LAPACK
- Added an API to let you query if there's already a registration for
a function, added a test to check that we register_meta for all
decompositions (except detach, that decomp is wrong lol), and then
update all the necessary sites to make the test pass.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77477
Approved by: https://github.com/zou3519
Decompositions can be used to fill in meta support where necessary,
assuming the operations they decompose to support meta key.
This PR adds register_meta kwarg to register_decomposition that
optionally lets you register the meta to the C++ dispatch table
for meta tensors. I use this to then get the meta function for
where and huber_loss for free.
Signed-off-by: Edward Z. Yang <ezyangfb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77353
Approved by: https://github.com/mruberry
