The goal of this PR is to provide a standard way to create simple treespec instances and hide the implementation details of the `PyTreeSpec` class.
Changes:
1. Add function `treespec_leaf()` to replace `LeafSpec()`.
2. Add function `treespec_tuple(...)` and `treespec_dict(...)` to create treespec for `tuple` / `dict` which is used for `*args` / `**kwargs`. This avoids direct modification to `treespec` instances that rely on the implementation details of the `PyTreeSpec` class.
3. Change `len(spec.children_specs)` to `spec.num_children`.
4. Change `isinstance(spec, LeafSpec)` to `spec.is_leaf()`.
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160843
Approved by: https://github.com/mlazos
The goal of this PR is to provide a standard way to create simple treespec instances and hide the implementation details of the `PyTreeSpec` class.
Changes:
1. Add function `treespec_leaf()` to replace `LeafSpec()`.
2. Add function `treespec_tuple(...)` and `treespec_dict(...)` to create treespec for `tuple` / `dict` which is used for `*args` / `**kwargs`. This avoids direct modification to `treespec` instances that rely on the implementation details of the `PyTreeSpec` class.
3. Change `len(spec.children_specs)` to `spec.num_children`.
4. Change `isinstance(spec, LeafSpec)` to `spec.is_leaf()`.
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160843
Approved by: https://github.com/mlazos
The goal of this PR is to provide a standard way to create simple treespec instances and hide the implementation details of the `PyTreeSpec` class.
Changes:
1. Add function `treespec_leaf()` to replace `LeafSpec()`.
2. Add function `treespec_tuple(...)` and `treespec_dict(...)` to create treespec for `tuple` / `dict` which is used for `*args` / `**kwargs`. This avoids direct modification to `treespec` instances that rely on the implementation details of the `PyTreeSpec` class.
3. Change `len(spec.children_specs)` to `spec.num_children`.
4. Change `isinstance(spec, LeafSpec)` to `spec.is_leaf()`.
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/160843
Approved by: https://github.com/mlazos
Resolves#164972
- #164972
All `torch.utils._cxx_pytree` functions are based on `optree` functions with hardcoded `none_is_leaf=True` and `namespace="torch"`. This PR changes the polyfills to generic `optree` functions with those arguments unhardcoded. This means `torch.utils._cxx_pytree` functions are still traceable while the community `optree` usages can get dynamo support additionally.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165860
Approved by: https://github.com/Lucaskabela
If optree is less than the minimum version, we should pretend it doesn't
exist.
The problem right now is:
- Install optree==0.12.1
- `import torch._dynamo`
- This raise an error "min optree version is 0.13.0"
The fix is to pretend optree doesn't exist if it is less than the min
version.
There are ways to clean up this PR more (e.g. have a single source of
truth for the version, some of the variables are redundant), but I am
trying to reduce the risk as much as possible for this to go into 2.7.
Test Plan:
I verified the above problem was fixed. Also tried some other things,
like the following, which now gives the expected behavior.
```py
>>> import torch
>>> import optree
>>> optree.__version__
'0.12.1'
>>> import torch._dynamo
>>> import torch._dynamo.polyfills.pytree
>>> import torch.utils._pytree
>>> import torch.utils._cxx_pytree
ImportError: torch.utils._cxx_pytree depends on optree, which is
an optional dependency of PyTorch. To u
se it, please upgrade your optree package to >= 0.13.0
```
I also audited all non-test callsites of optree and torch.utils._cxx_pytree.
Follow along with me:
optree imports
- torch.utils._cxx_pytree. This is fine.
- [guarded by check] f76b7ef33c/torch/_dynamo/polyfills/pytree.py (L29-L31)
_cxx_pytree imports
- [guarded by check] torch.utils._pytree (changed in this PR)
- [guarded by check] torch/_dynamo/polyfills/pytree.py (changed in this PR)
- [guarded by try-catch] f76b7ef33c/torch/distributed/_functional_collectives.py (L17)
- [guarded by try-catch] f76b7ef33c/torch/distributed/tensor/_op_schema.py (L15)
- [guarded by try-catch] f76b7ef33c/torch/distributed/tensor/_dispatch.py (L35)
- [guarded by try-catch] f76b7ef33c/torch/_dynamo/variables/user_defined.py (L94)
- [guarded by try-catch] f76b7ef33c/torch/distributed/tensor/experimental/_func_map.py (L14)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151257
Approved by: https://github.com/malfet, https://github.com/XuehaiPan
Changes in this PR:
1. Add `is_structseq` and `is_structseq_class` functions to determine a object or a class is PyStructSequence.
2. Add a generic class `structseq` which can be used as the registration key for PyStructSequence types like `namedtuple` for Named Tuple types.
3. Change `is_namedtuple` to accept subclasses of namedtuple to be namedtuple. Before this PR, only namedtuple class directly created by `collections.namedtuple` or `typing.NamedTuple` were namedtuple classes while their subclasses were not. This PR makes `is_namedtuple` return true for subclasses of namedtuple class.
Resolves#75982. New tests are included in this PR.
- #75982
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113257
Approved by: https://github.com/zou3519
Changes in this PR:
1. Add `is_structseq` and `is_structseq_class` functions to determine a object or a class is PyStructSequence.
2. Add a generic class `structseq` which can be used as the registration key for PyStructSequence types like `namedtuple` for Named Tuple types.
3. Change `is_namedtuple` to accept subclasses of namedtuple to be namedtuple. Before this PR, only namedtuple class directly created by `collections.namedtuple` or `typing.NamedTuple` were namedtuple classes while their subclasses were not. This PR makes `is_namedtuple` return true for subclasses of namedtuple class.
Resolves#75982. New tests are included in this PR.
- #75982
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113257
Approved by: https://github.com/zou3519
Changes in this PR:
1. Add `is_structseq` and `is_structseq_class` functions to determine a object or a class is PyStructSequence.
2. Add a generic class `structseq` which can be used as the registration key for PyStructSequence types like `namedtuple` for Named Tuple types.
3. Change `is_namedtuple` to accept subclasses of namedtuple to be namedtuple. Before this PR, only namedtuple class directly created by `collections.namedtuple` or `typing.NamedTuple` were namedtuple classes while their subclasses were not. This PR makes `is_namedtuple` return true for subclasses of namedtuple class.
Resolves#75982. New tests are included in this PR.
- #75982
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113257
Approved by: https://github.com/zou3519
This enables a check that which a class which only inherits from immutable classes like str, tuple, and NamedTuple, also defined `__slots__` so they don't allocate memory unnecessarily. This also ensure contributors think about how they define their classes with subclass NamedTuples and str, of which we have many in our codebase
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146276
Approved by: https://github.com/aorenste
