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
This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796 Enum contains duplicate value: {value}
PIE808 Unnecessary start argument in range
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796 Enum contains duplicate value: {value}
PIE808 Unnecessary start argument in range
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
This is follow-up of #165037. It generally recommended to use `is/is not` to compare types. Therefore this series of changes apply this suggestion in the code base, and it aims to finally enabling related linter checks.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165142
Approved by: https://github.com/albanD
Summary: D79674759 tried to fix the expensive prepare and convert steps, as `assert_and_get_unique_device` was called multiple times. This change fixes that issue by using `functools.cache` decorator.
Test Plan:
Verified on llm export to QNN.
LLM Quantization prepare time of ~20min reduced to ~3min.
Rollback Plan:
Differential Revision: D82073679
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162550
Approved by: https://github.com/andrewor14
Summary: This PR introduces shape guards to export. Previously only value ranges, equalities, and specializations would be tracked for symbolic expressions, and we had a forward hook to check them. Instead now we create a function to check shape guards and call it in the exported program.
Test Plan:
updated several tests
Rollback Plan:
Differential Revision: D80713603
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161178
Approved by: https://github.com/tugsbayasgalan
**Summary:** Previously, we call `assert_and_get_unqiue_device` once per node in both prepare and convert. This is expensive and unnecessary since the model device is the same across all nodes, so we should just call this once in the beginning and reuse the same model device across all the nodes.
**Test Plan:**
python test/test_quantization.py -k TestQuantizePT2E
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159901
Approved by: https://github.com/jerryzh168
Beginning of process for 3.14 bringup.
State of things from this PR:
- Nothing too scary looking from the Dynamo CPython side, nothing we heavily rely on seems to be missing @williamwen42
- The existing check that makes torch.compile() nicely fail is working as expected. So all these empty functions shouldn't cause any weirdness.
- The `__module__` update changes look suspicious, we should investigate what is the reason and impact of that, in particular for our public API checking @jbschlosser
- Leaving the weakref.py thread safety change as a follow up to keep this a bit simpler. I vendored the whole struct in the meantime FYI @ezyang
EDIT: The `__module__` change is even more cursed than I though due to changes to Union and Optional type where the `__module__` field cannot be changed anymore. See https://github.com/python/cpython/issues/132139 for details.
For now, I'm just skipping the `__module__` setting for 3.14 which will trip the public API checks. Will revisit once I have a final answer on the cpython issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158184
Approved by: https://github.com/msaroufim
