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
1. add a python meta registration, to fix an issue with the forward pass. The problem was that previously, the C++ meta registration calls [numel()](7b14a14e27/aten/src/ATen/native/TensorAdvancedIndexing.cpp (L329)) which fails (LMK if it's better to fix the C++ implementation to not do this check)
2. Modify the backward to fix an issue in the backward. The backward is not a custom op - it's a custom manual backward implementation. In particular, there's some situations that don't support double backward; the check for whether double backward is allowed requires a .item() call. To fix the meta/fake tensor case, this PR will avoid setting the double backward error only if `GradMode::is_enabled()` - which shouldn't be turned on in PT2.
3. Update skips.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106429
Approved by: https://github.com/zou3519
Currently there are FFT operators which raise `UnsupportedOperatorException`
because their meta implementations sometimes give incorrect strides. This works
around the problem for static shapes by falling back to eager. Though we still
don't support calls with dynamic shapes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106319
Approved by: https://github.com/ezyang
Previously, x.size(0) could return a SymInt, even when the internal
sympy expression was actually already constant (e.g., due to an
introduced guard.) We now allow to query the Python object with
maybe_as_int which allows us to transmute these objects back to
int when possible.
It is still possible to end up with a constant SymInt even after this
change, e.g., if you get out a SymInt and while holding onto it
specialize it, but casual users are more likely to get ints when they
want to.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104828
Approved by: https://github.com/Skylion007
Previously, x.size(0) could return a SymInt, even when the internal
sympy expression was actually already constant (e.g., due to an
introduced guard.) We now allow to query the Python object with
maybe_as_int which allows us to transmute these objects back to
int when possible.
It is still possible to end up with a constant SymInt even after this
change, e.g., if you get out a SymInt and while holding onto it
specialize it, but casual users are more likely to get ints when they
want to.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104828
Approved by: https://github.com/Skylion007
Previously, x.size(0) could return a SymInt, even when the internal
sympy expression was actually already constant (e.g., due to an
introduced guard.) We now allow to query the Python object with
maybe_as_int which allows us to transmute these objects back to
int when possible.
It is still possible to end up with a constant SymInt even after this
change, e.g., if you get out a SymInt and while holding onto it
specialize it, but casual users are more likely to get ints when they
want to.
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104828
Approved by: https://github.com/Skylion007
This PR introduces value range refinement of shape symbols by symbolically evaluating the
value range of the involved guards. This should help `_maybe_evaluate_static` to eliminate
more guards.
This is a stack of PRs created from the discussion on: #96616.
In summary, this PR:
- simplifies `FloorDiv` nodes on the left-hand side of an expression so as to isolate a
symbol in the numerator
- tries to match the expression against the form: `<symbol> <relop> <expr>`
- uses the matched expression for refining the value range of `<symbol>` using the range
of `<expr>`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/97963
Approved by: https://github.com/ezyang
This PR turns translation validation on by default for tests and accuracy benchmark
runs. It also installs Z3 on CI.
The main changes are:
- Add `--no-translation-validation` as an option in _test/run_tests.py_
- Set `PYTORCH_TEST_WITH_TV` environment variable
- Add `TEST_WITH_TV` variable in _torch/testing/_internal/common_utils.py_
- Turn translation validation on for accuracy benchmarks in _benchmarks/dynamo/common.py_
- Add Z3 installation on CI scripts
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103611
Approved by: https://github.com/ezyang
This PR adds support for `enable_grad`/`no_grad`/`autocast` context managers getting properly traced in `pre_dispatch` tracing. The stuff in this PR includes:
- I added a torch function mode that runs during make_fx pre_dispatch tracing, `ProxyTorchFunctionMode`. It directly intercepts the torch ops that run during the above context managers, and adds them to the current graph instead of executing them
- `enable_grad` and `no_grad` currently desugar into `torch._C.set_grad_enabled(bool)`, but this API isn't currently overrideable by torch function so I added the ability to interpose there
- the `torch.amp` context managers don't currently have a nice equivalent, like `set_autocast_enabled(state)`, so I ended up adding two new API's: `torch.amp._set_autocast_enabled` and `torch.amp._set_autocast_disabled`. If you look at how the context manager is implemented, it ends up calling several different state-changing functions, some of which depend on the backend - so I figured that it would be cleaner just to add a new API (that should probably only be used by tracing) - but open to feedback
- I added a new dynamo backend, `compile(backend="pre_dispatch_eager")`. When pre_dispatch tracing becomes always-on in inductor, it will be another potential surface for bugs. I also added a test file for it (`test/dynamo/test_pre_dispatch.py`).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103024
Approved by: https://github.com/ezyang
This is in preparation for the custom_op_compile_check utility, which
will call the newly refactored function.
This PR:
- splits off code into helper functions
- adds clearer error messages
- stops updating the inputs destructively (leading to slightly slower
tests)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103196
Approved by: https://github.com/bdhirsh, https://github.com/soulitzer
