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 diff moves export run_decompositions to use aot_export_joint_with_descriptors instead of aot_export_module. Doing so, i ran into 2 main bugs:
1) aot_export_joint_with_descriptors don't correctly pass in record_nn_module_stack flag that is needed to populate nn_module_stack by switching the internal tracer.
2) When creating symint with negative inputs, we need to pass in positive=False. This didn't matter before because aot_autograd directly returns integer inputs instead of creating symint.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165931
Approved by: https://github.com/zhxchen17
This diff moves export run_decompositions to use aot_export_joint_with_descriptors instead of aot_export_module. Doing so, i ran into 2 main bugs:
1) aot_export_joint_with_descriptors don't correctly pass in record_nn_module_stack flag that is needed to populate nn_module_stack by switching the internal tracer.
2) When creating symint with negative inputs, we need to pass in positive=False. This didn't matter before because aot_autograd directly returns integer inputs instead of creating symint.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165931
Approved by: https://github.com/zhxchen17
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
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/165037
Approved by: https://github.com/mlazos
Changelog:
1. When we run into an operation we didn't proxy, we end up emitting fake constants. We error under a config and we disable the config for some internal users. The reason we want to error is this signals a coverage problem we need to address but at the same time, we don't wnat to be disruptive to already working flows.
2. Previous attribute mutation detection logic in non-strict didn't account for nested module structure. This fixes silent incorrectness issue of exporting esm and qwen in non-strict and some torchbench models like levit_128 and demucs.
3. Previous logic didn't work on the cases where we mutate a container attribute as the previous approach used to pytree over old and new attributes resulting in length mismatch. We gracefully handle this now.
Differential Revision: [D83673054](https://our.internmc.facebook.com/intern/diff/D83673054)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164372
Approved by: https://github.com/avikchaudhuri
This adds basic support for subclass inputs in export (specifically for non-strict). I had to make fakify little more complicated which risks further divergence from dynamo fakification. But dynamo one is so complex, so i feel it is better to do this way. Also improved fake mode detection logic to recursively look into subclass inner tensors.
Differential Revision: [D83156489](https://our.internmc.facebook.com/intern/diff/D83156489)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163770
Approved by: https://github.com/avikchaudhuri
This makes gemma3 exportable on transformers=4.55.4
In HF, there is a torch funciton mode called TransformGetItemToIndex which internally calls custom autograd function. When this custom autograd function is called under vmap, It triggers CustomFunctionHigherOrderOP which error-ed because there was no pre-dispatch proxy mode implementation.
Since there are number of requests lately to add various operators in pre-dispatch IR, I introduce a decorator in export that works similar to `allow_in_graph`. Basically:
1) We intercept custom_autograd_function.apply at pre-dispatch mode when this decorator is applied
2) We apply `flat_apply` HOP to hide the pytree spec for this autograd function. Note that this adds restriction that this custom autograd function needs to take in fx-able types.
3) subclass constructor decorator is implemented similarly, so we just refactor it to use similar implementation as this new decorator. eventually we should delete the subclass constructor decorator.
4) Move some code in subclass constructor decorator to exit early in non-export environment which should shave off some inefficiency (around 1% according to @swolchok 's benchmark)
Fixes: https://github.com/pytorch/pytorch/issues/161563#issuecomment-3246309758
Differential Revision: [D82141316](https://our.internmc.facebook.com/intern/diff/D82141316)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162240
Approved by: https://github.com/ydwu4
