Summary:
Reference https://github.com/pytorch/pytorch/issues/38349
Delegates to `torch.transpose` (not sure what is the best way to alias)
TODO:
* [x] Add test
* [x] Add documentation
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46041
Reviewed By: gchanan
Differential Revision: D25022816
Pulled By: mruberry
fbshipit-source-id: c80223d081cef84f523ef9b23fbedeb2f8c1efc5
Summary:
`torch.lu_solve` now works for complex inputs both on CPU and GPU.
I moved the existing tests to `test_linalg.py` and modified them to test complex dtypes, but I didn't modify/improve the body of the tests.
Ref. https://github.com/pytorch/pytorch/issues/33152
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46862
Reviewed By: nikithamalgifb
Differential Revision: D24543682
Pulled By: anjali411
fbshipit-source-id: 165bde39ef95cafebf976c5ba4b487297efe8433
Summary:
`torch.triangular_solve` now works for complex inputs on GPU.
I moved the existing tests to `test_linalg.py` and modified them to test complex and float32 dtypes.
Ref. https://github.com/pytorch/pytorch/issues/33152
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46916
Reviewed By: navahgar, agolynski
Differential Revision: D24706647
Pulled By: anjali411
fbshipit-source-id: fe780eac93d2ae1b2549539bb385e5fac25213b3
Summary:
Related: https://github.com/pytorch/pytorch/issues/33090
I just realized that I haven't updated the docs of `torch.eig` when implementing the backward.
Here's the PR updating the docs about the grad of `torch.eig`.
cc albanD
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47598
Reviewed By: heitorschueroff
Differential Revision: D24829373
Pulled By: albanD
fbshipit-source-id: 89963ce66b2933e6c34e2efc93ad0f2c3dd28c68
Summary:
`torch.inverse` now works for complex inputs on GPU.
Test cases with complex matrices are xfailed for now. For example, batched matmul does not work with complex yet.
Ref. https://github.com/pytorch/pytorch/issues/33152
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45034
Reviewed By: zou3519
Differential Revision: D24730264
Pulled By: anjali411
fbshipit-source-id: b9c94ec463012913c117278a884adeee96ea02aa
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46758
It's in general helpful to support int32 indices and offsets, especially when such tensors are large and need to be transferred to accelerator backends. Since it may not be very useful to support the combination of int32 indices and int64 offsets, here we enforce that these two must have the same type.
Test Plan: unit tests
Reviewed By: ngimel
Differential Revision: D24470808
fbshipit-source-id: 94b8a1d0b7fc9fe3d128247aa042c04d7c227f0b
Summary:
This PR adds a function for calculating the Kronecker product of tensors.
The implementation is based on `at::tensordot` with permutations and reshape.
Tests pass.
TODO:
- [x] Add more test cases
- [x] Write documentation
- [x] Add entry `common_methods_invokations.py`
Ref. https://github.com/pytorch/pytorch/issues/42666
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45358
Reviewed By: mrshenli
Differential Revision: D24680755
Pulled By: mruberry
fbshipit-source-id: b1f8694589349986c3abfda3dc1971584932b3fa
Summary:
Related https://github.com/pytorch/pytorch/issues/38349
This PR implements `column_stack` as the composite ops of `torch.reshape` and `torch.hstack`, and makes `row_stack` as the alias of `torch.vstack`.
Todo
- [x] docs
- [x] alias pattern for `row_stack`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46313
Reviewed By: ngimel
Differential Revision: D24585471
Pulled By: mruberry
fbshipit-source-id: 62fc0ffd43d051dc3ecf386a3e9c0b89086c1d1c
Summary:
This PR adds support for complex-valued input for `torch.pinverse`.
Fixed cuda SVD implementation to return singular values with real dtype.
Fixes https://github.com/pytorch/pytorch/issues/45385.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45819
Reviewed By: heitorschueroff
Differential Revision: D24306539
Pulled By: anjali411
fbshipit-source-id: 2fe19bc630de528e0643132689e1bc5ffeaa162a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45847
Original PR here https://github.com/pytorch/pytorch/pull/45084. Created this one because I was having problems with ghstack.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D24136629
Pulled By: heitorschueroff
fbshipit-source-id: dd7c7540a33f6a19e1ad70ba2479d5de44abbdf9
Summary:
Many of our functions contain same warnings about results reproducibility. Make them use common template.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45748
Reviewed By: colesbury
Differential Revision: D24089114
Pulled By: ngimel
fbshipit-source-id: e6aa4ce6082f6e0f4ce2713c2bf1864ee1c3712a
Summary:
**BC-breaking note**
For ease of exposition let a_min be the value of the "min" argument to clamp, and a_max be the value of the "max" argument to clamp.
This PR changes the behavior of torch.clamp to always compute min(max(a, a_min), a_max). torch.clamp currently computes this in its vectorized CPU specializations:
78b95b6204/aten/src/ATen/cpu/vec256/vec256_double.h (L304)
but in other places it clamps differently:
78b95b6204/aten/src/ATen/cpu/vec256/vec256_base.h (L624)78b95b6204/aten/src/ATen/native/cuda/UnaryOpsKernel.cu (L160)
These implementations are the same when a_min < a_max, but divergent when a_min > a_max. This divergence is easily triggered:
```
t = torch.arange(200).to(torch.float)
torch.clamp(t, 4, 2)[0]
: tensor(2.)
torch.clamp(t.cuda(), 4, 2)[0]
: tensor(4., device='cuda:0')
torch.clamp(torch.tensor(0), 4, 2)
: tensor(4)
```
This PR makes the behavior consistent with NumPy's clip. C++'s std::clamp's behavior is undefined when a_min > a_max, but Clang's std::clamp will return 10 in this case (although the program, per the above comment, is in error). Python has no standard clamp implementation.
**PR Summary**
Fixes discrepancy between AVX, CUDA, and base vector implementation for clamp, such that all implementations are consistent and use min(max_vec, max(min_vec, x) formula, thus making it equivalent to numpy.clip in all implementations.
The same fix as in https://github.com/pytorch/pytorch/issues/32587 but isolated to the kernel change only, so that the internal team can benchmark.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43288
Reviewed By: colesbury
Differential Revision: D24079453
Pulled By: mruberry
fbshipit-source-id: 67f30d2f2c86bbd3e87080b32f00e8fb131a53f7
Summary:
This PR makes the deprecation warnings for existing fft functions more prominent and makes the torch.stft deprecation warning consistent with our current deprecation planning.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45409
Reviewed By: ngimel
Differential Revision: D23974975
Pulled By: mruberry
fbshipit-source-id: b90d8276095122ac3542ab625cb49b991379c1f8
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/39955
resolves https://github.com/pytorch/pytorch/issues/36323 by adding `torch.sgn` for complex tensors.
`torch.sgn` returns `x/abs(x)` for `x != 0` and returns `0 + 0j` for `x==0`
This PR doesn't test the correctness of the gradients. It will be done as a part of auditing all the ops in future once we decide the autograd behavior (JAX vs TF) and add gradchek.
Test Plan: Imported from OSS
Reviewed By: mruberry
Differential Revision: D23460526
Pulled By: anjali411
fbshipit-source-id: 70fc4e14e4d66196e27cf188e0422a335fc42f92
Summary:
These alias are consistent with NumPy. Note that C++'s naming would be different (std::multiplies and std::divides), and that PyTorch's existing names (mul and div) are consistent with Python's dunders.
This also improves the instructions for adding an alias to clarify that dispatch keys should be removed when copying native_function.yaml entries to create the alias entries.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44463
Reviewed By: ngimel
Differential Revision: D23670782
Pulled By: mruberry
fbshipit-source-id: 9f1bdf8ff447abc624ff9e9be7ac600f98340ac4
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44393
torch.quantile now correctly propagates nan and implemented torch.nanquantile similar to numpy.nanquantile.
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D23649613
Pulled By: heitorschueroff
fbshipit-source-id: 5201d076745ae1237cedc7631c28cf446be99936
Summary:
This PR:
- updates div to perform true division
- makes torch.true_divide an alias of torch.div
This follows on work in previous PyTorch releases that first deprecated div performing "integer" or "floor" division, then prevented it by throwing a runtime error.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/42907
Reviewed By: ngimel
Differential Revision: D23622114
Pulled By: mruberry
fbshipit-source-id: 414c7e3c1a662a6c3c731ad99cc942507d843927
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44562
Add a note that torch.median returns the smaller of the two middle elements for even-sized input and refer user to torch.quantile for the mean of the middle values.
fixes https://github.com/pytorch/pytorch/issues/39520
Test Plan: Imported from OSS
Reviewed By: albanD
Differential Revision: D23657208
Pulled By: heitorschueroff
fbshipit-source-id: 2747aa652d1e7f10229d9299b089295aeae092c2
Summary:
This fixes a `katex` error I was getting trying to build the docs:
```
ParseError: KaTeX parse error: Undefined control sequence: \0 at position 55: …gin{cases}
```
This failure was introduced in https://github.com/pytorch/pytorch/issues/42523.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/44481
Reviewed By: colesbury
Differential Revision: D23627700
Pulled By: mruberry
fbshipit-source-id: 9cc09c687a7d9349da79a0ac87d6c962c9cfbe2d
Summary:
**BC-breaking note**
This change is BC-breaking for C++ callers of linspace and logspace if they were providing a steps argument that could not be converted to an optional.
**PR note**
This PR deprecates calling linspace and logspace wihout setting steps explicitly by:
- updating the documentation to warn that not setting steps is deprecated
- warning (once) when linspace and logspace are called without steps being specified
A test for this behavior is added to test_tensor_creation_ops. The warning only appears once per process, however, so the test would pass even if no warning were thrown. Ideally there would be a mechanism to force all warnings, include those from TORCH_WARN_ONCE, to trigger.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/43860
Reviewed By: izdeby
Differential Revision: D23498980
Pulled By: mruberry
fbshipit-source-id: c48d7a58896714d184cb6ff2a48e964243fafc90
