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
Reference cycles are freed by the cycle collector rather than being cleaned up
when the objects in the cycle first become unreachable. If a cycle points to a tensor,
the CUDA memory for that tensor will not be freed until garbage collection runs.
Accumulation of CUDA allocations can lead to out of memory errors (OOMs), as well as
non-deterministic allocation behavior which is harder to debug.
This visualizer installs a garbage collection hook to look for cycles containing
CUDA tensors and saves a visualization of the garbage:
```
from torch.cuda._cycleviz import warn_tensor_cycles
warn_tensor_cycles()
# do some work that results in a cycle getting garbage collected
# ...
> WARNING:root:Reference cycle includes a CUDA Tensor see visualization of cycle /tmp/tmpeideu9gl.html
```
Reland to make windows skip the test.
This reverts commit 7b3b6dd426.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104051
Approved by: https://github.com/aaronenyeshi, https://github.com/malfet
Reference cycles are freed by the cycle collector rather than being cleaned up
when the objects in the cycle first become unreachable. If a cycle points to a tensor,
the CUDA memory for that tensor will not be freed until garbage collection runs.
Accumulatin of CUDA allocations can lead to out of memory errors (OOMs), as well as
non-deterministic allocation behavior which is harder to debug.
This visualizer installs a garbage collection hook to look for cycles containing
CUDA tensors and saves a visualization of the garbage:
```
from torch.cuda._cycleviz import warn_tensor_cycles
warn_tensor_cycles()
# do some work that results in a cycle getting garbage collected
# ...
> WARNING:root:Reference cycle includes a CUDA Tensor see visualization of cycle /tmp/tmpeideu9gl.html
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102656
Approved by: https://github.com/aaronenyeshi
