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
Summary:
Original commit changeset: d6d62d0c96dd
Original Phabricator Diff: D84468451 and D84613184
D84468451 caused CUDA OutOfMemoryError in model.
Test Plan:
D84468451 was found through bisect. Also double checked on recent trunk 9866939225248c2adc307be7a804b26db0b9b555: f815887517
With this diff that backs out D84468451 and D84613184 : f816114560
Differential Revision: D85025378
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165910
Approved by: https://github.com/clee2000
Reapply of https://github.com/pytorch/pytorch/pull/163260
AOTI utils expect free function sometimes so adjust export API to handle that, haven't seen any methods getting exported. Some AOTI flows also require we populate dynamo_flat_name_to_original_fqn so i just copy how it is done in eval_frame.py. I also cleaned up how we get rid of export_root and fixed some overcomplicated nn_module_stack handling in export code. The logic is simpler now thanks to @anijain2305 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165582
Approved by: https://github.com/anijain2305
This fixes AOTAutograd rms_norm not being bitwise equivalent to
eager, because it avoids a decomposition. You can force the
decomposition by having the decomposition in the dispatch table,
but if eager mode wouldn't have decomposed (because it went to the fused
one), we now default to preserving the fused call by default.
This largely reverts https://github.com/pytorch/pytorch/pull/103275/ for view ops. This means that in inference mode we could hit the wrong C++ kernel; if this occurs we should just SymInt'ify the C++ kernel.
Another neat side effect of this change is that Inductor's generated kernels for rms_norm now have rms_norm in their name.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164939
Approved by: https://github.com/bdhirsh
Summary: While saving state_dict tensors, deduping is done to reduce number of tensor data. For this storage point is used. But when the tensor is empty, storage pointer is 0. But dtype of the tensors could be different. Existing logic will consider all such tensor as same. This will fail the model later when different dtype is expected. This change will include dtype also while deduping. For non empty tensor, this should not affect as the storage point will be unique.
Test Plan: TBD
Differential Revision: D84243094
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165090
Approved by: https://github.com/yiming0416
Some more context at https://github.com/pytorch/pytorch/pull/164939
The basic point here is that Python decomps are guaranteed to be functional, whereas C++ ones are not. If we have a Python decomp, we should prefer it over the C++ one. This currently doesn't matter too much as CIA decomps will get functionalized, but it matters after the quoted PR because we now run these decompositions very late (to make it easy for things like aot_eager to get the fused versions of operators in proxy tensor).
Signed-off-by: Edward Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164970
Approved by: https://github.com/bdhirsh
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
This fixes AOTAutograd rms_norm not being bitwise equivalent to
eager, because it avoids a decomposition. You can force the
decomposition by having the decomposition in the dispatch table,
but if eager mode wouldn't have decomposed (because it went to the fused
one), we now default to preserving the fused call by default.
This largely reverts https://github.com/pytorch/pytorch/pull/103275/ for view ops. This means that in inference mode we could hit the wrong C++ kernel; if this occurs we should just SymInt'ify the C++ kernel.
Another neat side effect of this change is that Inductor's generated kernels for rms_norm now have rms_norm in their name.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164939
Approved by: https://github.com/bdhirsh
After lean export, we might want to be able to restore the original fqn. This PR refactors one util function in export that sort of does this. Note that strict_export has some complicated logic of updating the graph signature as well which we don't want. I think we can gradually make this util more refined by handling constants, non persistent buffers etc and change how strict_export does it today.
Differential Revision: [D83687844](https://www.internalfb.com/diff/D83687844)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164401
Approved by: https://github.com/avikchaudhuri
This fixes AOTAutograd rms_norm not being bitwise equivalent to
eager, because it avoids a decomposition. You can force the
decomposition by having the decomposition in the dispatch table,
but if eager mode wouldn't have decomposed (because it went to the fused
one), we now default to preserving the fused call by default.
This largely reverts https://github.com/pytorch/pytorch/pull/103275/ for view ops. This means that in inference mode we could hit the wrong C++ kernel; if this occurs we should just SymInt'ify the C++ kernel.
Another neat side effect of this change is that Inductor's generated kernels for rms_norm now have rms_norm in their name.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164939
Approved by: https://github.com/bdhirsh
ghstack dependencies: #164573
This PR is to temporarily unblock various experiments to re-use dynamo create fake mode. Note that this is still not what we want as the end state. The end state should look sth like:
```
out = fulllgraph_capture(mod, inputs)
fake_mode = out.backend_inputs.fake_mode
gm = out.module()
```
This doesn't work today because export requires we need to wrap the original module to setup a flat module to trace for easier handling of pytree. As a result, we would need to carry export specific flag in fullgraph_capture which seems not ideal.
Regardless, the end state is that we need to give downstream user a graph module and a fake mode in some form, so I think _dynamo_graph_capture_for_export returning a fake mode within graph module itself via gm.meta
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164730
Approved by: https://github.com/avikchaudhuri
Summary:
Previously, weight deduplication was done by simply grouping tensors with their untyped storage and saving the first tensor in the group.
A more rigorous approach would be to find a complete tensor that covers the storage and store that tensor. This is particularly important for GPU weights because when saving to raw bytes, we move the weight to CPU first, and if the weight being saved is not a complete one, it will lose the storage information during the copy to CPU.
In this diff, we reuse code in `_package_weights.py` for better weights and constants deduplication in `torch.export.save`.
Test Plan: buck2 run mode/dev-nosan caffe2/test:test_export -- -r test_weight_sharing_gpu
Differential Revision: D83523690
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164196
Approved by: https://github.com/angelayi
