To codegen deferred runtime asserts, I need to be able to convert sympy expressions back into regular Python expressions that I can put in FX graphs. This PR adds some of the machinery to do this: it adds a new sympy analysis that runs operations on all FX traceable operations that can also be run with plain Python int/float/bool/etc. It's tested by symbolic tracing through the analysis, and then testing that this traced graph gives the same result as running the Python analysis directly.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113978
Approved by: https://github.com/aakhundov, https://github.com/lezcano
Currently meta_utils relies on as_strided when handling the view case (recursively meta-ify the base, and then do as_strided to simulate the view), but NestedTensor does not support as_strided today (though maybe it could?), so what we want to do instead is call Tensor. _view_func. Conveniently, _view_func IS always available for nested tensors.
A detail to note is that _view_func actually incurs a guard because it needs to perform some metadata checks to make sure the view is still valid. This PR adds Tensor._unsafe_view_func which can avoid that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/112205
Approved by: https://github.com/jbschlosser
This PR supports sym_ite. This is useful for converting SymBool to SymInt in e.g. #109916. Internally, it uses sympy.Piecewise. We cannot use sympy.ITE because it expects the arguments and output all to be boolean type but we want return SymInt type when converting a SymBool to SymInt. So we use sympy.Piecewise to denote the symbolic relationship.
Note that this pr uses the range analysis for sympy.Piecewise implemented in https://github.com/pytorch/pytorch/blob/main/torch/utils/_sympy/value_ranges.py.
Test Plan:
See added test.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111440
Approved by: https://github.com/ezyang
This pr expose torch._higher_order_ops.cond as torch.cond.
1. Need to add #noqa: F811 to the _check calls in torch/__init__.py to address some confusing linter error "Redefinition of unused 'cond'" but only one cond is imported and for these lines that have this error, they don't define the cond but just use it as an argument.
2. Also add cond to the list that allows it to be traced through so as dynamo could trigger the CondHigherOrder logic instead of creating a TorchVariable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110293
Approved by: https://github.com/zou3519
This pr expose torch._higher_order_ops.cond as torch.cond.
1. Need to add #noqa: F811 to the _check calls in torch/__init__.py to address some confusing linter error "Redefinition of unused 'cond'" but only one cond is imported and for these lines that have this error, they don't define the cond but just use it as an argument.
2. Also add cond to the list that allows it to be traced through so as dynamo could trigger the CondHigherOrder logic instead of creating a TorchVariable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110293
Approved by: https://github.com/zou3519
Fixes#109842.
This disables the implicit `UserWarning`s that were raised for deprecated `torch` attributes. The filtering was designed to be as specific as possible, in order to not filter any other warnings that may be raised.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109890
Approved by: https://github.com/ezyang
Summary:
FBGEMM uses `self.iter.is_cuda` to check if the tensor is for CUDA. This diff enables similar feature `self.iter.is_mtia` for tensors with MTIA device key.
Test Plan: See diff D48693225
Reviewed By: jackm321
Differential Revision: D48809191
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108310
Approved by: https://github.com/albanD
Summary:
This is a stride based attribute for a tensor available in Python.
This can help inspect tensors generated using `torch.empty_permuted(.., physical_layout, ...)`, where physical_layout should match the dim_order returned here. `empty_permuted` will be renamed to use dim_order as the param name in the future. And also help Executorch export pipeline with implementing dim_order based tensors.
Differential Revision: D48134476
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106835
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
Some notable changes:
1. `constrain_as_size` allows min value to be less than 2 as it will unconditionally assume min >= 2 for compiler purposes. Instead, we add additional check to make sure max value is always greater than 2.
2. Previously, we used to runtime assert on the unbacked symint's val range which would be always between [2, max]. I modified this logic to assert on [0, max] unless user explicitly specifies the min range.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106591
Approved by: https://github.com/gmagogsfm, https://github.com/ezyang
Some notable changes:
1. `constrain_as_size` allows min value to be less than 2 as it will unconditionally assume min >= 2 for compiler purposes. Instead, we add additional check to make sure max value is always greater than 2.
2. Previously, we used to runtime assert on the unbacked symint's val range which would be always between [2, max]. I modified this logic to assert on [0, max] unless user explicitly specifies the min range.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106591
Approved by: https://github.com/gmagogsfm, https://github.com/ezyang
As part of this, a new `AutocastIPU` dispatch key has been added.
There's an existing PR, #85043, to make `Autocast` a proper per-backend functionality key, but it ran into issues with layering with other functionality keys and went stale.
This has been tested in the out-of-tree IPU PyTorch backend.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103890
Approved by: https://github.com/albanD
At high current implementation of constrains functions (constrain_as_**) will raise exception for the following code snippets:
```
def f(x):
a = x.item()
constrain_as_size(a, 4, 7)
return torch.empty((a, 4))
inp = torch.tensor([5])
ep = torch._export.export(f, (inp,))
```
The reason is because current constrain logic is:
1) Purely python so it won't survive AOT export (the full node is gone after AOT export since AOT export only maintains aten level op).
2) Utilize side effect to add range constraints for traced symbol's shape env ([code](9591e52880/torch/fx/experimental/symbolic_shapes.py (L370-L372))).
3) If runtime assertion is turned on (by default). [`_AddRuntimeAssertionsForConstraintsPass`](9591e52880/torch/_export/passes/add_runtime_assertions_for_constraints_pass.py (L98-L100)) will try to append assertion node based on range constrains extracted from shape env of symbol during another interpretation round.
4). However, since 1), in the round of AOT export, range constraints logic won't run for symbols generated during this round. And later there is no range constrains information available for assertion round and caused issue.
5) As a result of above, it will failure at `torch.empty((a, 4))` (there is no constrains for `a` that it must be positive).
The fix here is just to implement range constrain logic as a native aten op (CPU implementation as no-op) to make it be able to survive AOT export.
**NOTE:**
[Logic](2d745b95d7/torch/fx/experimental/symbolic_shapes.py (L350-L365C15)) within [`constrain_range`](2d745b95d7/torch/fx/experimental/symbolic_shapes.py (LL313C74-L313C74)) is split out as `constrain_range_int` to capture case when non `SymInt` is passed in and reused in the new `_constrain_range`. The reason is when non `SymInt` is provided:
* If it directly calls `sym_constrain_range`, the C++ version will be called which will be no-op.
* So in this case it calls `constrain_range_int` instead to be able to capture issue like user provides a input whose tensor's shape could be out of range during exporting, like the following for above code example:
```
...
inp = torch.tensor([10])
ep = torch._export.export(f, (inp,)) # immediately raise error
```
Differential Revision: [D46734204](https://our.internmc.facebook.com/intern/diff/D46734204)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103346
Approved by: https://github.com/tugsbayasgalan
