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
Summary: Treespec can be reused instead of calculated from str every AOTI module call. Using cached result saves 0.2ms for each module call.
Test Plan:
Before:
{F1974751578}
After:
{F1974751667}
Differential Revision: D68749539
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145815
Approved by: https://github.com/henrylhtsang
Changes:
1. Bump `ruff` from 0.7.4 to 0.8.4
2. Change `%`-formatted strings to f-string
3. Change arguments with the `__`-prefix to positional-only arguments with the `/` separator in function signature.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143753
Approved by: https://github.com/Skylion007
0.12.0 Major Updates:
- Add context manager to temporarily set the dictionary sorting mode
- Add accessor APIs
- Use `stable` tag for `pybind11` for Python 3.13 support
- Fix potential segmentation fault for pickling support
0.12.1 Updates:
- Fix warning regression during import when launch with strict warning filters
Closes#130155
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130139
Approved by: https://github.com/zou3519
ghstack dependencies: #130895
0.12.0 Major Updates:
- Add context manager to temporarily set the dictionary sorting mode
- Add accessor APIs
- Use `stable` tag for `pybind11` for Python 3.13 support
- Fix potential segmentation fault for pickling support
0.12.1 Updates:
- Fix warning regression during import when launch with strict warning filters
Closes#130155
Pull Request resolved: https://github.com/pytorch/pytorch/pull/130139
Approved by: https://github.com/zou3519
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
Resolves#126888
- #126888
This PR is split from PR #126898.
- #126898
------
Pull Request resolved: https://github.com/pytorch/pytorch/pull/127689
Approved by: https://github.com/Skylion007
Use `typing_extensions.deprecated` for deprecation annotation if possible. Otherwise, add `category=FutureWarning` to `warnings.warn("message")` if the category is missing.
Note that only warnings that their messages contain `[Dd]eprecat(ed|ion)` are updated in this PR.
UPDATE: Use `FutureWarning` instead of `DeprecationWarning`.
Resolves#126888
- #126888
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126898
Approved by: https://github.com/albanD
In many places in the code we use `tree_map_only((SymInt, SymBool, SymFloat), foo)` but with nested ints, it is possible to have SymInts that are non-symbolic, so we may want to do something like `tree_map_only(is_symbolic, foo)` instead.
Alternative: wrap nested int SymNodes with something other than SymInt.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119974
Approved by: https://github.com/zou3519
ghstack dependencies: #119661
Fixes#119768
- #119768
This PR adds a new function `tree_iter` that lazily iterates over the tree leaves. It is different than the `tree_leaves` function while the latter traversal the whole tree first to build a list of leaves.
```python
for leaf in tree_iter(tree):
...
```
is much more efficient than:
```python
for leaf in tree_leaves(tree):
...
```
where `tree_leaves(tree)` is `list(tree_iter(tree))`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/120155
Approved by: https://github.com/vmoens
This PR introduces a key path API to pytrees, drawing direct inspiration from JAX's [key path API](https://jax.readthedocs.io/en/latest/jax-101/05.1-pytrees.html#key-paths).
I added the 3 APIs described there, and a registry of `flatten_with_keys` fns for each node type, which is a version of `flatten` that also returns `KeyEntry`s describing how to access values from the original pytree.
Current use cases for this API:
- Folks would like to do argument traversal over input pytrees to do verification and compatibility enforcement. Keypaths are useful for this—https://fburl.com/code/06p7zrvr is a handrolled pass doing basically the same thing but probably more fragilely.
- In export non-strict mode, we need to figure out a way to track sources for pytree inputs. In strict mode, dynamo handles this for us, but we'd like a decoupled component to handle this when we're not using dynamo.
I'm sure there are places it would be useful.
Some design notes:
- I only implemented the API for the Python pytree impl. optree has some differences in how their keypath APIs are designed (see https://github.com/pytorch/pytorch/issues/113378 for discussion). I have some issues with the proposed typed_path solution in that discussion and prefer JAX's API, but we can hash that out separately.
- The way folks register a `flatten_with_keys` fn is through a new kwarg to `register_pytree_node`. This follows how we do serialization fns, although the list of additional arguments is getting unwieldy.
- My impl handles pytrees with an undefined `flatten_with_keys` fn is different from JAX. I will raise an error, JAX creates a fallback keyentry.
Differential Revision: [D52547850](https://our.internmc.facebook.com/intern/diff/D52547850/)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/116786
Approved by: https://github.com/voznesenskym
Changes:
1. Add `_private_register_pytree_node` API in both C++ and Python pytree. In C++ pytree, the API will only register pytree node for C++ pytree. In Python pytree, the API will only register pytree node for Python pytree.
2. Do not allow registering a type as pytree node twice in the Python pytree.
3. Add thread lock to the Python pytree node register API.
4. The old `_register_pytree_node` API will call the `_private_register_pytree_node` API and raise a deprecation warning.
5. Add a new `register_pytree_node` API to register node type in both C++ and Python implementations.
6. Add tests to ensure a warning will be raised when the old private function is called.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112111
Approved by: https://github.com/zou3519
It appears that `mypy` is now checking a few more previously-unchecked files; these files
are being found via import-following. Not sure exactly why they weren't being checked before.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/114160
Approved by: https://github.com/eellison
ghstack dependencies: #114162
Changes:
1. Add `_private_register_pytree_node` API in both C++ and Python pytree. In C++ pytree, the API will only register pytree node for C++ pytree. In Python pytree, the API will only register pytree node for Python pytree.
2. Do not allow registering a type as pytree node twice in the Python pytree.
3. Add thread lock to the Python pytree node register API.
4. The old `_register_pytree_node` API will call the `_private_register_pytree_node` API and raise a deprecation warning.
5. Add a new `register_pytree_node` API to register node type in both C++ and Python implementations.
6. Add tests to ensure a warning will be raised when the old private function is called.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112111
Approved by: https://github.com/zou3519
Reorganized the two C++ and Python pytree submodules into a subpackage. I think this would be easier to implement the abstract `PyTreeAPI` class with two implementations. And it will be much easier for the user to switch between the two implementations.
Before:
```text
torch
├── utils
│ ├── _pytree.py
│ ├── _cxx_pytree.py
│ ...
...
```
After:
```text
torch
├── utils
│ ├── _pytree
│ │ ├── __init__.py
│ │ └── api
│ │ ├── __init__.py
│ │ ├── cxx.py
│ │ └── python.py
│ ...
...
```
The `torch.utils._pytree` module will import all APIs from `torch.utils._pytree.api.python`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112278
Approved by: https://github.com/zou3519
ghstack dependencies: #112111
Reorganized the two C++ and Python pytree submodules into a subpackage. I think this would be easier to implement the abstract `PyTreeAPI` class with two implementations. And it will be much easier for the user to switch between the two implementations.
Before:
```text
torch
├── utils
│ ├── _pytree.py
│ ├── _cxx_pytree.py
│ ...
...
```
After:
```text
torch
├── utils
│ ├── _pytree
│ │ ├── __init__.py
│ │ └── api
│ │ ├── __init__.py
│ │ ├── cxx.py
│ │ └── python.py
│ ...
...
```
The `torch.utils._pytree` module will import all APIs from `torch.utils._pytree.api.python`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112278
Approved by: https://github.com/zou3519
ghstack dependencies: #112111
Custom classes that are serialized with pytree are serialized by default with `f”{class.__module__}.{class.__name__}”`. This is a dependency from our serialized program directly into the outer Python environment. If a user moves the class to a different directory, the serialized program will be unable to be loaded. So, we will require users to pass in an FQN if they want to serialize their custom treespec type.
Differential Revision: [D50886366](https://our.internmc.facebook.com/intern/diff/D50886366)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112428
Approved by: https://github.com/suo
