This avoids generation of bad bytecode, leading to really confusing
error. I am not sure why we can't reconstruct cleanly, it has to do with
the input being a dict, while other supported ctx managers take bools.
Fixing that is for another day. Lets give a good error message for now.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166006
Approved by: https://github.com/yushangdi, https://github.com/SherlockNoMad
Builds on top of https://github.com/pytorch/pytorch/pull/163673 and https://github.com/pytorch/pytorch/pull/164174. This will be used in the followup PRs to apply regional inductor compilation.
The existing implementation let Dynamo trace into the `torch.fx.traceback.annotate`, but thats not what we want. We want Dynamo to essentially run the torch.fx.traceback.annotate function in eager, so that every Fx node created in Dynamo Fx graph has the custom meta node.
What does not work?
* We still have to set the context manager `torch.fx.traceback.preserve_node_meta()` in the user code because CI was unhappy. This can be fixed but with some perseverance.
* This does not work with graph breaks yet. But we can solve that problem, if needed, in a separate PR.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164678
Approved by: https://github.com/SherlockNoMad, https://github.com/jansel, https://github.com/xmfan
Renaming `set_fullgraph` to `error_on_graph_break` for now. There are no semantic differences yet. In a followup PR, we will introduce a new `torch.compile` option `error_on_graph_break` that has lower priority than `fullgraph` so that `fullgraph` really returns 1 graph.
I could keep `set_fullgraph` as a deprecated alias for `error_on_graph_break` for now, but I'm hoping that won't be necessary since it's still private API (there are no internal callsites yet, and there are no significant OSS callsites yet).
cc @albanD @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @chauhang @amjames @Lucaskabela @mlazos @guilhermeleobas @xmfan as primary users for `set_fullgraph`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/161739
Approved by: https://github.com/xmfan, https://github.com/Lucaskabela, https://github.com/anijain2305, https://github.com/mlazos
Summary: ONNX team and recent transformer upgrade ran into this error and we also ran into during our export benchmarking. This diff makes it possible to trace through vmap implementation in pre-dispatch IR. Note that we don't support serializing functorch ops in pre-dispatch IR and in the future, we should desugar them to post-grad ops.
The implementation strategy is:
1. We add python wrappers around vmap APIs so that we attach custom torch function handler that is only on during non-strict export. The reason is we don't want to add this to default torch_function handler because it will break BC.
2. Some dynamo changes to make sure it picks up new python wrapper APIs. The reason is when we do strict export, we need to re-materialize these APIs in pre-dispatch IR from torch IR. We can avoid this by special casing in dynamo for export to proxy different API calls but i feel that is too much chaos because you need to be able to proxy 2 different variants of same vmap API.
Test Plan: CI
Differential Revision: D75623875
Pull Request resolved: https://github.com/pytorch/pytorch/pull/154650
Approved by: https://github.com/ezyang, https://github.com/zou3519
Implement traceable config patching for Dynamo: enables restricted patching of Dynamo config where user can use a context manager/decorator to change tracing behavior for parts of the code.
The new `dont_skip_tracing` decorator/context manager for ignoring most trace rules is easily implemented with this more generic traceable config patching feature.
Implementation:
- Create a new specialized context manager class representing a wrapper around torch._dynamo.config.patch
- Dynamo doesn't trace into the context manager but updates config at compile time
- Correctness is based on our correctness for handling supported context managers
- Implementation is inspired by how `GradModeVariable` is implemented.
Previous attempts: https://github.com/pytorch/pytorch/pull/148736 (decorator-only global approach) and https://github.com/pytorch/pytorch/pull/149439 (decorator-only traceback approach)
See https://docs.google.com/document/d/1vWNwKL_jpg-PLopifcaSa338wks3GqSVF4GHRguybGg/edit?tab=t.0 for more details on implementation - including previous approaches.
NOTE: this PR fixes a bug where skipped code objects were not tracked by convert_frame.py, leading to cases where code objects would be automatically skipped even after `torch._dynamo.reset()`. This exposed some latent dynamo-wrapped test failures in CI that previously passed in CI but not locally.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150586
Approved by: https://github.com/jansel, https://github.com/zou3519, https://github.com/anijain2305
Implement traceable config patching for Dynamo: enables restricted patching of Dynamo config where user can use a context manager/decorator to change tracing behavior for parts of the code.
The new `dont_skip_tracing` decorator/context manager for ignoring most trace rules is easily implemented with this more generic traceable config patching feature.
Implementation:
- Create a new specialized context manager class representing a wrapper around torch._dynamo.config.patch
- Dynamo doesn't trace into the context manager but updates config at compile time
- Correctness is based on our correctness for handling supported context managers
- Implementation is inspired by how `GradModeVariable` is implemented.
Previous attempts: https://github.com/pytorch/pytorch/pull/148736 (decorator-only global approach) and https://github.com/pytorch/pytorch/pull/149439 (decorator-only traceback approach)
See https://docs.google.com/document/d/1vWNwKL_jpg-PLopifcaSa338wks3GqSVF4GHRguybGg/edit?tab=t.0 for more details on implementation - including previous approaches.
NOTE: this PR fixes a bug where skipped code objects were not tracked by convert_frame.py, leading to cases where code objects would be automatically skipped even after `torch._dynamo.reset()`. This exposed some latent dynamo-wrapped test failures in CI that previously passed in CI but not locally.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150586
Approved by: https://github.com/jansel, https://github.com/zou3519, https://github.com/anijain2305
PR does following
* Turns `inference_mode` to False and `no_grad` for `convert_frame`, if the inference_mode is on globally.
* Turns off inference_mode for fake tensor prop. This ensures that converting from real inference tensor to a fake tensor removes the inference-ness.
* Graph breaks on is_inference and is_inference_mode_enabled.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149321
Approved by: https://github.com/jansel, https://github.com/zou3519
See the comment [here](https://github.com/pytorch/pytorch/issues/132014#issuecomment-2379547400) (cc @H-Huang @awgu @kwen2501 @wanchaol @fegin @fduwjj @wz337 @wconstab @d4l3k @c-p-i-o @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @ColinPeppler @amjames @desertfire @chauhang @aakhundov @XilunWu @rec) - this PR updates `_unsafe_set_version_counter` to accept a list of tensors, for overhead-sensitive users (e.g. distributed) who need to hide VC bumps from autograd on a large list of tensors without wanting to suffer the overhead of going from python->C++ separately for every tensor in the list.
I left the binding in pybind, and used a `std::vector`. if we **really** need to optimize overhead even further, we could write a manual cpython binding.
I use this updated API in the next PR to fix FSDP2, so that it properly hides the VC of all `all_gather_buffer` tensors in its call to `split_with_sizes_copy.out(all_gather_buffers)`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/137921
Approved by: https://github.com/awgu, https://github.com/albanD
Prior to this patch, Dynamo conveniently modelled torch profiler context
objects (e.g., `torch.profiler.profile`) as `NullContextVariable`
because `torch.compile` ignore the effect of these profiler contexts.
However, the semantics of these profiler contexts diverges from
`contextlib.nullcontext` in the `__enter__` function, where the former
returns `self` and the latter returns `None`. This causes subtle error
as observed in #125021.
This patch adds back a `ProfilerContextVariable`, which addresses the
aforementioned semantic discrepency.
Fixes#125021.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145537
Approved by: https://github.com/zou3519, https://github.com/williamwen42
**Overview**
This PR moves `torch/distributed/_composable/fsdp` to `torch/distributed/fsdp/_fully_shard` and makes public APIs available from `torch.distributed.fsdp`, e.g.:
```
from torch.distributed.fsdp import fully_shard
```
This is targeting 2.6 release. I rewrote some of the documentation with (hopefully) improved phrasing.
**Changes for Reland**
- Preserved the public objects from `torch/distributed/_composable/fsdp/fully_shard.py` so that the import path still works internally
- Added a unit test that we can do `from torch.distributed._composable.fsdp.fully_shard import FSDPModule`
Differential Revision: [D66890387](https://our.internmc.facebook.com/intern/diff/D66890387)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141868
Approved by: https://github.com/kwen2501, https://github.com/wconstab, https://github.com/weifengpy, https://github.com/fegin, https://github.com/XilunWu
Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
