This PR adds support for list subclasses. Among other things are
1) Tracking the mutations on internal vts like `_dict_vt` and `_list_vt` using sources. This helps identify if there was a mutation in the underlying data structures, and we need to reconstruct it.
2) `UserDefinedObjectVariable` now has a new method - `is_modified` which `side_effect` infra relies upon to check mutations in the underlying vts (like `_dict_vt`).
3) `reconstruction` logic ensures that we use `dict.__getitem__` and `list.__getitem__` methods. This is super important because we don't want to call the overridden `__getitem__` methods.
If this PR is hard to review, please let me know. I can break it into several small PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/146819
Approved by: https://github.com/StrongerXi, https://github.com/jansel
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
Dynamo wasn't handling the new PEP585 type annotations:
```
x = list[Foo]
```
Although this worked in py3.9 this was causing an `unimplemented` (Unexpected type in sourceless builder) in py3.12.
This fixes it to treat them as a BuiltinVariable.
Fixes#145226
Pull Request resolved: https://github.com/pytorch/pytorch/pull/145240
Approved by: https://github.com/anijain2305
In hinsight, we never needed a DICT_SUBCLASS_GUARD_MANAGER, because Dynamo would inline through the overridden keys method. In this PR, we ensure that while creating guards and constructing variable trackers, we get the `d.keys()` value by using `dict.keys(d)`. This ensures that we do not call overridden keys method. Therefore, the C++ guard can use `PyDict_Next` directly to check the guards.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143722
Approved by: https://github.com/jansel
In hinsight, we never needed a DICT_SUBCLASS_GUARD_MANAGER, because Dynamo would inline through the overridden keys method. In this PR, we ensure that while creating guards and constructing variable trackers, we get the `d.keys()` value by using `dict.keys(d)`. This ensures that we do not call overridden keys method. Therefore, the C++ guard can use `PyDict_Next` directly to check the guards.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143722
Approved by: https://github.com/jansel
Resolves issue #140464 by adding an option to not specialize int from nn.Modules (False by default to maintain existing behavior).
Test Plan: `buck2 test mode/opt caffe2/test/dynamo:test_dynamo -- test_modules.py::NNModuleTests::test_nn_module_unspec_int_attr`
Differential Revision: D66837042
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142829
Approved by: https://github.com/ezyang, https://github.com/yanboliang
Fixes https://github.com/pytorch/pytorch/issues/141305.
```python
class M(torch.nn.Module):
def forward(self, x, y, z):
a = y.shape[0]
b = z.shape[0]
def true_fn(x):
return x + a
def false_fn(x):
return x + b * z
# When exporting with non-strict: a and b are symints,
# so torch.compile need to wrap and trace symint inputs.
return torch.cond(x.shape[0] > 5, true_fn, false_fn, (x,))
```
In non-strict export, when inputs are annotated with dynamic shape, the a, and b in above example are torch.SymInt type. true_fn and false_fn will have closure that're of torch.SymInt types. The error is triggered because we didn't handle SymInt inputs in dynamo and ends up using a UserDefinedObjectVariable for it, which doesn't have a proxy. We added support by following how we handle SymBool input previously.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141524
Approved by: https://github.com/zou3519
ghstack dependencies: #142185
This adds an option to cause automatic dynamic shapes to trigger
unbacked SymInts rather than backed SymInts. This can potentially
help if you are still seeing recompilations from 0/1 specialization
but it also might just cause your program to fail with
GuardOnDataDependent errors.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141415
Approved by: https://github.com/bobrenjc93
We introduced a special graph break to avoid max-recursion-depth error
in #100296.
After #111415, the original `test_list_self_reference` no longer
triggers the special graph break because we started modeling root frame
free variables with `LazyVariableTracker`.
After #117426, we no longer build the list items eagerly, and they'll hit
`variable_tracker_cache` when they get lazily constructed later.
As a result, this patch updates the `test_list_self_reference` test and
removes the special graph break.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142438
Approved by: https://github.com/jansel
ghstack dependencies: #142437
Dynamo was generating `GetItemSource(tuple_source, index)` for items of
`NamedTupleVariable`, but that stops working when a user supplied named
tuple has a custom `__getitem__` function with different semantics.
This patch
- fixes the aforementioned issue by using `AttrSource` instead.
- handles named tuple outside `wrap_listlike`, by removing the special
case of named tuple in `BaseListVariable.cls_for_instance`, since the
semantics of named tuple is different enough.
- makes user all constructions of `NamedTupleVariable` has items with
proper sources.
Fixes#142399.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142437
Approved by: https://github.com/jansel
Fixes https://github.com/pytorch/pytorch/issues/141305.
```python
class M(torch.nn.Module):
def forward(self, x, y, z):
a = y.shape[0]
b = z.shape[0]
def true_fn(x):
return x + a
def false_fn(x):
return x + b * z
# When exporting with non-strict: a and b are symints,
# so torch.compile need to wrap and trace symint inputs.
return torch.cond(x.shape[0] > 5, true_fn, false_fn, (x,))
```
In non-strict export, when inputs are annotated with dynamic shape, the a, and b in above example are torch.SymInt type. true_fn and false_fn will have closure that're of torch.SymInt types. The error is triggered because we didn't handle SymInt inputs in dynamo and ends up using a UserDefinedObjectVariable for it, which doesn't have a proxy. We added support by following how we handle SymBool input previously.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141524
Approved by: https://github.com/zou3519
ghstack dependencies: #141610, #142185
This PR fixes the issue where AOTAutograd would produce a guard that used a symbolic value
that came from one of the input's base.
```python
@torch.compile(backend="aot_eager", dynamic=True)
def f(a, b):
a.add_(1)
b.add_(1)
return a
x = torch.ones(10)
f(x[1:], x[1:])
```
In the example above, AOTAutograd functionalizes the mutation by making use of
`as_strided_scatter` operation, which produces the guard: `s0 >= s1 + 1`, where:
- `s0`: corresponds to `x.size()[0]`
- `s1`: corresponds to `a.size()[0]`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139554
Approved by: https://github.com/bdhirsh
As title, this also uncovered a few invalid use cases; the cases that
cause error are fixed in separate patches prior to this patch, and the
rest are fixed in this patch.
This patch also moves a few `.source` mutation to variable construction,
to increase the coverage of the validation.
Fixes#133027.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141717
Approved by: https://github.com/jansel
ghstack dependencies: #141713, #141714, #141715, #141902, #141716
A subsequeunt patch attempts to fix a side-effect issue for range
iterators, which in turn exposed an exising issue on guards for range
iterators -- the following test started failing:
```
PYTORCH_TEST_WITH_DYNAMO=1 python test/test_tensor_creation_ops.py TestTensorCreationCPU.test_hstack_column_stack_cpu_int16
```
This patch adds a `RANGE_ITERATOR_MATCH` guard to make sure that we
properly guard on range iterators, and adds a regression test.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141902
Approved by: https://github.com/jansel
ghstack dependencies: #141713, #141714, #141715
Previously we never replayed side effects to `DequeVariable` with a
source; the bug was already in the `test_deque_input` test, but went
unnoticed because we didn't check the deque objects.
This patch adds limited but practical support for this (see comments in
`side_effects.py` for why limited), and updates the deque tests to check
for this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/141714
Approved by: https://github.com/jansel
ghstack dependencies: #141713
