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
This PR get rids of the dim_groups attribute from DeviceMesh, the main
motivation behind this is that we should let c10d store the process
groups during its creation instead of DeviceMesh, DeviceMesh should just
handle ranks correctly.
This could enable DTensor becomes picklable! (torch.save/load could be
possible), which I will give it a try in the next PR
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103105
Approved by: https://github.com/XilunWu, https://github.com/fduwjj
This is the first series of PR that adopts operator impls to use a
strategy based approach, each op utilizes OpStrategy and PlacementStrategy
to generate their own strategy. By utilizing the strategy based
approach along with the op graph, we could enable more advanced op
implementation (decomp is possible), and turn the sharding prop to be
more like a contraint satisfication problem.
This PR alone only adds some basic tensor op strategies, and it directly
works on the op graph that was used for metadata propagation. The tensor ops
added in this PR mainly follows one of the arg strategy. The next set of
PRs would add more op strategies to other ops.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100607
Approved by: https://github.com/XilunWu
To make TP more generic for Attention module, we come up with this new col/rowwise parallel style.
Basically, the idea behind is that:
We only do DTensor op for Col/Rowwise sharded part. For the rest of ATen ops, we will leave it to Tensor ops.
And we set this behavior as default for Colwise and Rowwise parallel style. If people want to customize it, they can always pass in different prepare_input or prepare_output
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100508
Approved by: https://github.com/wanchaol
## What's in this PR
DeviceMesh's __init__ function now requires all calling ranks to pass the same `mesh` argument.
## Why
We want to enforce SPMD style of programs using DTensor. Before this PR, 2-D Parallel API (e.g. _create_1d_device_mesh) defines different DeviceMesh on different ranks. After this PR, it defines each sub-meshes and simply perform communications on the one that it is associated with.
Differential Revision: [D45165511](https://our.internmc.facebook.com/intern/diff/D45165511)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/99094
Approved by: https://github.com/wanchaol
Preparation for the next PR in this stack: #89559.
I replaced
- `self.assertTrue(torch.equal(...))` with `self.assertEqual(..., rtol=0, atol=0, exact_device=True)`,
- the same for `self.assertFalse(...)` with `self.assertNotEqual(...)`, and
- `assert torch.equal(...)` with `torch.testing.assert_close(..., rtol=0, atol=0)` (note that we don't need to set `check_device=True` here since that is the default).
There were a few instances where the result of `torch.equal` is used directly. In that cases I've replaced with `(... == ...).all().item()` while sometimes also dropping the `.item()` depending on the context.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89527
Approved by: https://github.com/mruberry
