This is by no means comprehensive, but adds initial support for SymInt as a Scalar.
Things that don't work yet but need to:
- for some reason `torch.add(tensor, sym_int)` got matched to the `add.Tensor(Tensor self, Tensor other, *, Scalar alpha=1) -> Tensor` schema
- `x + sym_int` failed bc we tried to turn `x` into a sym int:
```
"__radd__",
[](c10::SymIntNode a, py::object b) -> c10::SymIntNode {
auto snb = toSymIntNode(a, b);
return a->add(snb);
})
```
- Many more things I'm sure
Pull Request resolved: https://github.com/pytorch/pytorch/pull/84958
Approved by: https://github.com/ezyang
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56830
Opt into formatting on GitHub and format everything. This is a trial run before turning on formatting for more and eventually all of the codebase.
Test Plan: CI
Reviewed By: zertosh
Differential Revision: D27979080
fbshipit-source-id: a80f0c48691c08ae8ca0af06377b87e6a2351151
Summary:
All pretty minor. I avoided renaming `class DestructableMock` to `class DestructibleMock` and similar such symbol renames (in this PR).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49815
Reviewed By: VitalyFedyunin
Differential Revision: D25734507
Pulled By: mruberry
fbshipit-source-id: bbe8874a99d047e9d9814bf92ea8c036a5c6a3fd
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46596
1. Added `conj` method for scalar similar to numpy.
2. Updates backward formulas for add and sub to work correctly for R -> C cases and for the case when alpha is complex.
3. Enabled complex backward for nonzero (no formula update needed).
Test Plan: Imported from OSS
Reviewed By: glaringlee
Differential Revision: D24529227
Pulled By: anjali411
fbshipit-source-id: da871309a6decf5a4ab5c561d5ab35fc66b5273d
Summary:
**Main:**
- `c10::complex` is refactored: it no longer uses inheritance to specialize constructors, but using SFINAE instead. This implementation is cleaner and avoids some compiler bugs.
- `c10::Scalar` is cleaned up: it no longer needs to store complex as `double z[2]`, `c10::complex<double>` will work.
**Other cleanups:**
- `numeric_limits` of `c10::complex` is moved to `complex_utils.h`
- the variable in `c10::complex` storing real and imag is changed from `storage[2]` to `real_` and `imag_`
- remove the `c10::` before `complex` when in `c10` namespace
Pull Request resolved: https://github.com/pytorch/pytorch/pull/38593
Differential Revision: D21769463
Pulled By: anjali411
fbshipit-source-id: 3cb5bcbb0ff304d137221e00fe481a08dba7bc12
Summary:
# Overview
This PR changes the backing type of complex tensors in `ScalarType` from `std::complex` to `c10::complex`.
Since `c10::complex` and `std::complex` are reinterpret-castable, we can freely use `std::complex *` to access `c10::complex` data and vice versa. The implementation of `c10::complex` is not complete yet, so we are reinterpret casting all complex data to `std::complex` during dispatch, and do all operations in `std::complex`.
# `std::complex` and `c10::complex` interoperatability
To use `std::complex *` to access `c10::complex` data, the following specializations are added:
```C++
template <> inline std::complex<float>* Tensor::data_ptr();
template <> inline std::complex<double>* Tensor::data_ptr();
template <> inline std::complex<float> Tensor::item();
template <> inline std::complex<double> Tensor::item();
```
See [`aten/src/ATen/templates/TensorMethods.h`](https://github.com/pytorch/pytorch/pull/37274/files#diff-0e8bf6f5024b32c240a4c1f0b4d8fd71)
And
```C++
template <> inline std::complex<float> Scalar::to();
template <> inline std::complex<double> Scalar::to();
```
is added in [`c10/core/Scalar.h`](https://github.com/pytorch/pytorch/pull/37274/files#diff-aabe1c134055c8dcefad830c1c7ae957)
# Dispatch
Macros in [`Dispatch.h`](https://github.com/pytorch/pytorch/pull/37274/files#diff-737cfdab7707be924da409a98d46cb98) still using `std::complex` as its type. We will add macros such as `AT_DISPATCH_ALL_TYPES_AND_C10_COMPLEX_AND3` as needed during the migration and not in this PR.
Note that `AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND3` is only used in copy kernel of CUDA, and this PR is already changing it to use `c10::complex` because CUDA copy kernel has to use its original dtype otherwise there will be funny casting of dtypes causing cuda unspecified launch error.
When all the migration is done, the c10 version of macros will be removed, and the default version will have `std::complex` replaced by `c10::complex` by default. This design allows us to incrementally migrate from `std::complex` to `c10::complex`.
# Note
Note that the `std::complex` is not completely replaced by `c10::complex` in c10 yet, for example `c10::Scalar` is still using `std::complex`. This will be fixed in later PRs.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37421
Differential Revision: D21282161
Pulled By: anjali411
fbshipit-source-id: 635e309e8c8a807c2217723ad250b5ab5a20ce45
Summary:
Improve handling of mixed-type tensor operations.
This PR affects the arithmetic (add, sub, mul, and div) operators implemented via TensorIterator (so dense but not sparse tensor ops).
For these operators, we will now promote to reasonable types where possible, following the rules defined in https://github.com/pytorch/pytorch/issues/9515, and error in cases where the cast would require floating point -> integral or non-boolean to boolean downcasts.
The details of the promotion rules are described here:
https://github.com/nairbv/pytorch/blob/promote_types_strict/docs/source/tensor_attributes.rst
Some specific backwards incompatible examples:
* now `int_tensor * float` will result in a float tensor, whereas previously the floating point operand was first cast to an int. Previously `torch.tensor(10) * 1.9` => `tensor(10)` because the 1.9 was downcast to `1`. Now the result will be the more intuitive `tensor(19)`
* Now `int_tensor *= float` will error, since the floating point result of this operation can't be cast into the in-place integral type result.
See more examples/detail in the original issue (https://github.com/pytorch/pytorch/issues/9515), in the above linked tensor_attributes.rst doc, or in the test_type_promotion.py tests added in this PR:
https://github.com/nairbv/pytorch/blob/promote_types_strict/test/test_type_promotion.py
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22273
Reviewed By: gchanan
Differential Revision: D16582230
Pulled By: nairbv
fbshipit-source-id: 4029cca891908cdbf4253e4513c617bba7306cb3
