This is for "for some large number Z, make sure the error messages are readable English." - beginning to audit all `unimplemented` sites and making sure that all messages are at least English-readable. Hints may not necessarily be provided.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/147385
Approved by: https://github.com/jansel
Made the following changes:
- mutates_args is now keyword-only and mandatory. This is to align with
torch.library.custom_op (which makes it mandatory because it's easy to
miss)
- op_name is now keyword-only. This helps the readability of the API
- updated all usages of infer_schema
This change is not BC-breaking because we introduced
torch.library.infer_schema a couple of days ago.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130705
Approved by: https://github.com/yushangdi
ghstack dependencies: #131777
Made the following changes:
- mutates_args is now keyword-only and mandatory. This is to align with
torch.library.custom_op (which makes it mandatory because it's easy to
miss)
- op_name is now keyword-only. This helps the readability of the API
- updated all usages of infer_schema
This change is not BC-breaking because we introduced
torch.library.infer_schema a couple of days ago.
Test Plan:
- tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130705
Approved by: https://github.com/yushangdi
Fixes#105157
Bug source: `from __future__ import annotations` converts type annotation to strings to make forwards references easier. However, existing custom ops do not consider strings to be valid types.
Fix: We check if the argument and return type annotation is string type. If so, we try to use `eval` to convert it to a type.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128809
Approved by: https://github.com/zou3519
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
The `usort` config in `pyproject.toml` has no effect due to a typo. Fixing the typo make `usort` do more and generate the changes in the PR. Except `pyproject.toml`, all changes are generated by `lintrunner -a --take UFMT --all-files`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127126
Approved by: https://github.com/kit1980
ghstack dependencies: #127122, #127123, #127124, #127125
Previously, if someone used `register_fake` to add a fake impl for an
operator defined in C++, we would require them to add a
`m.set_python_module(<module>)` call to C++. This was to avoid
situations where a user imported the C++ operator without importing the
fake impl.
This "breaks" open registration: there's no way to add a fake impl
outside of a repository that defines an operator, so we want to turn
this behavior off by default in open source.
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/124064
Approved by: https://github.com/albanD
ghstack dependencies: #123937
Previously, if someone wrote a python abstract impl but didn't import
the module it is in, then we would raise an error message suggesting
that the user needs to add an abstract impl for the operator.
In addition to this, we suggest that the user try importing the module
associated with the operator in the pystub (it's not guaranteed that
an abstract impl does exist) to avoid confusion.
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/117770
Approved by: https://github.com/ydwu4, https://github.com/williamwen42
Summary:
We've made the following changes:
- The new way to use the API is `m.impl_abstract_pystub(module, context)`.
Every subsequent m.def of an op inside the TORCH_LIBRARY block gives
the op the `impl_abstract_pystub`.
- Added a mechanism to determine if an operator was defined in Python or C++.
Library.define in Python appends the op to a global set, which is analogous
to what we do for tracking Library.impl.
- If someone does `torch.library.impl_abstract` in Python for an operator, then
we require that it has an `impl_abstract_pystub` specified and we also check
that the module in the `impl_abstract_pystub` is the same as the module where
the call to `torch.library.impl_abstract` exists.
- Unfortunately we can't check the "context" (which is the buck target on
buck-based systems) because buck sits above us.
bypass-github-export-checks
Test Plan: - existing tests
Differential Revision: D51080493
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113182
Approved by: https://github.com/ezyang
Summary:
We've made the following changes:
- The new way to use the API is `m.impl_abstract_pystub(module, context)`.
Every subsequent m.def of an op inside the TORCH_LIBRARY block gives
the op the `impl_abstract_pystub`.
- Added a mechanism to determine if an operator was defined in Python or C++.
Library.define in Python appends the op to a global set, which is analogous
to what we do for tracking Library.impl.
- If someone does `torch.library.impl_abstract` in Python for an operator, then
we require that it has an `impl_abstract_pystub` specified and we also check
that the module in the `impl_abstract_pystub` is the same as the module where
the call to `torch.library.impl_abstract` exists.
- Unfortunately we can't check the "context" (which is the buck target on
buck-based systems) because buck sits above us.
Test Plan: - existing tests
Differential Revision: D50972148
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112851
Approved by: https://github.com/ezyang
Generally, to extend PyTorch with custom operators, a user will
create a Python module whose import triggers registration of
the custom operators via a torch.ops.load_library call or a call
to one or more torch.library.* APIs.
It is unexpected for Python modules to have side effects, so some
linters and formatters will complain. Use torch.ops.import_module to
import the module without a linter or formatter complaining.
NB: A more robust API would actually check if a custom op was registered
or modified, but this is technically challenging to do. In the future we
can add a warning if a custom op wasn't registered or modified.
Test Plan:
- existing tests
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110090
Approved by: https://github.com/ezyang
We want users to be able to define custom ops in C++ but put the
abstract impl in Python (since it is easier to write them in Python and
the abstract impl better models device semantics and data-dependent
operators).
`m.impl_abstract_pystub(opname, python_module, context)` declares the
abstract_impl of the operator to exist in the given python module.
When the abstract_impl needs to be accessed (either via FakeTensor or
Meta), and it does not exist, the PyTorch Dispatcher will yell
with a descriptive error message.
Some details:
- We construct a new global AbstractImplPyStub mapping in
Dispatcher.cpp. Read/write to this map is protected by the Dispatcher
lock.
- We add a new Meta Tensor fallback kernel. The fallback errors out if there is
no meta kernel, but also offers a nicer error message if we see that there is
a pystub.
- We create a `torch._utils_internal.throw_abstract_impl_not_imported_error`
helper function to throw errors. This way, we can throw different error
messages in OSS PyTorch vs internal PyTorch. To invoke this from C++, we
added a PyInterpreter::throw_abstract_impl_not_imported_error.
Differential Revision: [D49464753](https://our.internmc.facebook.com/intern/diff/D49464753/)
Differential Revision: [D49464753](https://our.internmc.facebook.com/intern/diff/D49464753)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109529
Approved by: https://github.com/ezyang, https://github.com/bdhirsh
This PR addresses the issues opened in #25155. However, those specific tests are no longer used since after #37473 they were moved to test_torchbind.
This PR enable TestTorchbind on Windows.
test_custom_class.py is no longer used after that commit.
In the original issue, there were problems on Windows with those tests so I tested the updated ones to see if they work.
I had no issues with them so this enables them on Windows.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/96507
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66744
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D31705358
fbshipit-source-id: d6ea350cbaa8f452fc78f238160e5374be637a48
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66234
Modified loops in files under fbsource/fbcode/caffe2/ from the format
`for(TYPE var=x0;var<x_max;x++)`
to the format
`for(const auto var: irange(xmax))`
This was achieved by running r-barnes's loop upgrader script (D28874212) with some modification to exclude all files under /torch/jit and a number of reversions or unused variable suppression warnings added by hand.
bypass_size_limit
allow-large-files
Test Plan: Sandcastle
Reviewed By: ngimel
Differential Revision: D30652629
fbshipit-source-id: 0ae6c4bbbb554bad42e372792a6430e1acf15e3e
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/63414
Misuse of raw pointer in here where stack is never nullable.
ghstack-source-id: 136938318
Test Plan:
compiles.
Imported from OSS
Reviewed By: ejguan
Differential Revision: D30375410
fbshipit-source-id: 9d65b620bb76d90d886c800f54308520095d58ee
Summary:
Relate to https://github.com/pytorch/pytorch/issues/50483.
Everything except ONNX, detectron and release notes tests are moved to use common_utils.run_tests() to ensure CI reports XML correctly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50923
Reviewed By: samestep
Differential Revision: D26027621
Pulled By: walterddr
fbshipit-source-id: b04c03f10d1fe96181b720c4c3868e86e4c6281a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48178
I migrated all call sites that used the legacy dispatcher registration API (RegisterOperators()) to use the new API (TORCH_LIBRARY...). I found all call-sites by running `fbgs RegisterOperators()`. This includes several places, including other OSS code (nestedtensor, torchtext, torchvision). A few things to call out:
For simple ops that only had one registered kernel without a dispatch key, I replaced them with:
```
TORCH_LIBRARY_FRAGMENT(ns, m) {
m.def("opName", fn_name);
}
```
For ops that registered to a specific dispatch key / had multiple kernels registered, I registered the common kernel (math/cpu) directly inside a `TORCH_LIBRARY_FRAGMENT` block, and registered any additional kernels from other files (e.g. cuda) in a separate `TORCH_LIBRARY_IMPL` block.
```
// cpu file
TORCH_LIBRARY_FRAGMENT(ns, m) {
m.def("opName(schema_inputs) -> schema_outputs");
m.impl("opName", torch::dispatch(c10::DispatchKey::CPU, TORCH_FN(cpu_kernel)));
}
// cuda file
TORCH_LIBRARY_IMPL(ns, CUDA, m) {
m.impl("opName", torch::dispatch(c10::DispatchKey::CUDA, TORCH_FN(cuda_kernel)));
}
```
Special cases:
I found a few ops that used a (legacy) `CPUTensorId`/`CUDATensorId` dispatch key. Updated those to use CPU/CUDA- this seems safe because the keys are aliased to one another in `DispatchKey.h`
There were a handful of ops that registered a functor (function class) to the legacy API. As far as I could tell we don't allow this case in the new API, mainly because you can accomplish the same thing more cleanly with lambdas. Rather than delete the class I wrote a wrapper function on top of the class, which I passed to the new API.
There were a handful of ops that were registered only to a CUDA dispatch key. I put them inside a TORCH_LIBRARY_FRAGMENT block, and used a `def()` and `impl()` call like in case two above.
Test Plan: Imported from OSS
Reviewed By: ezyang
Differential Revision: D25056090
Pulled By: bdhirsh
fbshipit-source-id: 8f868b45f545e5da2f21924046e786850eba70d9
