Earlier if there were no ops in the graph, fullgraph=True will also fallback to eager. This hides issues in testing, where we silently fallback to eager, and do not test optimized bytecode. As can be seen in the PR, I had to fix several tests when I forced to use the optimized bytecode in the absence of graph. A few failing tests will be fixed in follow up PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146527
Approved by: https://github.com/zou3519, https://github.com/StrongerXi
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
Fixes https://github.com/pytorch/pytorch/issues/144907
```
class Foo(torch.nn.Module):
def forward(self, val):
return torch.full((80, 2), val, dtype=torch.float32)
export(Foo(), args=(torch.tensor(1),))
```
When we have a `torch.full` call like above, where the fill value is a scalar Tensor and not a scalar value, the FX graph from `_dynamo.export()` contains a single node: the full op. We run into a `PendingUnbackedSymbolNotFound` error, because the `item()` call is implicit; the UnbackedSymInt is extracted but goes directly into the data of the output tensor value, and we're then unable to locate it when we try to compute unbacked bindings.
On the other hand, non-strict export doesn't face this, because an explicit `item()`, or `local_scalar_dense` node is inserted, and the unbacked binding is directly the example value of that node.
This adds a dynamo handler to imitate what happens in non-strict.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/144999
Approved by: https://github.com/angelayi
This reintroduces the change backed out by #145393 and fixes the underlying problem.
Although using a BuiltinVariable was better than nothing when we saw a GenericAlias it had problems if there was a graph break and we had to reconstruct the original python code which BuiltinVariable did as a simple `list` instead of a `list[int]`.
This changes it to use a TypingVariable instead and then teaches TypingVariable how to reconstruct.
Original commit changeset: 77b9193acb23
python test/dynamo/test_repros.py ReproTests.test_graph_break_on_jit_isinstance
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145554
Approved by: https://github.com/anijain2305
ghstack dependencies: #145551, #145552, #145553
BuiltinVariable.call_hasattr() overrides the base class - but actually behaves differently. The base is `obj.call_hasattr(tx, attr)` but BuiltinVariable's version is `<unused>.call_hasattr(tx, obj, attr)`.
The BuiltinVariable version is used as a pattern from `call_self_handler()` for `BuiltinVariable(hasattr)`. I think the other version is just used for internal `hasattr(obj, name)` so I renamed that one to `call_obj_hasattr`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145551
Approved by: https://github.com/anijain2305
This PR implements the user-facing dim change, i.e., that the scan dim provided by the user is always moved to dim 0 and then the associative_scan operation always operates on dim 0.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139864
Approved by: https://github.com/ydwu4
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
Pickling GraphModule needs some special handling for wrapping things that normally can't be pickled - but async compile needs to pass them across a wire so we need to be able to serialize it - add some helpers to enable that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141659
Approved by: https://github.com/jamesjwu
