This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04:
- Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh`
- Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
This PR re-lands
- [Typing] Fix PEP 484 Violation (#105022)
- Update mypy to 1.4.1 (#91983)
That were reverted due to the conflict with internal source repo.
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional)
Plus few real fixes:
- Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi`
- Add missing return statement to `torch._export. deserialize_graph`
- Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights`
- Add assert it `torch/optim/optimizer.py` that Optional list is not None
TODO (in followup PR):
- Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227
Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007
torch/custom_op.py is getting long, and the autograd pieces are going to
make it even longer. I'm planning on just organizing the files under
a torch/_custom_op folder.
Note that the imports now look a bit crazy (from torch._custom_op.impl
import...) but they will look more OK when we figure out the plan to
make custom_op public (coming later).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101823
Approved by: https://github.com/ezyang, https://github.com/albanD, https://github.com/bdhirsh
Previously, to specify e.g. int[], a user needed to do Tuple[int, ...].
This PR changes it to Sequence[int].
Bikeshedding: we could totally just use List[int] instead. The types
that the user gives us that we use to infer a schema is not entirely
faithful: for example, we convert `int` to SymInt.
I didn't feel strongly between Sequence[int] and List[int] so I went
with the more faithful one, plus Python recommends that you use Sequence
for input arguments (over list or tuple), though we don't subscribe to
that philosophy in general.
Test Plan:
- new test
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101190
Approved by: https://github.com/bdhirsh
This adds a new operator debugprims::load_storage which does the unusual thing of loading a tensor from disk (via ContentStoreReader). This will be used in a later PR to implement delta debugging in the minifier, even when the repro is too big to fit into memory. The way it works is that you specify a name of the tensor you want to load, as well as enough metadata to reconstruct the tensor, if the store isn't available. If there is an active content store, we read and return the tensor from that store; otherwise we use `rand_strided` to create it.
I needed some infra improvements to do this:
* `custom_op` now supports factory functions. Factory functions have to be registered specially via `impl_factory`
* I modified `clone_input` to also support dtype conversion, which I use to change the dtype of a loaded tensor if necessary.
* ContentStore needs to work with a device argument, so we torch.load directly to the correct device. This is for fake tensor support.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100519
Approved by: https://github.com/zou3519, https://github.com/anijain2305
