Summary:
The approach is to simply reuse `torch.repeat` but adding one more functionality to tile, which is to prepend 1's to reps arrays if there are more dimensions to the tensors than the reps given in input. Thus for a tensor of shape (64, 3, 24, 24) and reps of (2, 2) will become (1, 1, 2, 2), which is what NumPy does.
I've encountered some instability with the test on my end, where I could get a random failure of the test (due to, sometimes, random value of `self.dim()`, and sometimes, segfaults). I'd appreciate any feedback on the test or an explanation for this instability so I can this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47974
Reviewed By: ngimel
Differential Revision: D25148963
Pulled By: mruberry
fbshipit-source-id: bf63b72c6fe3d3998a682822e669666f7cc97c58
Summary:
Adds ldexp operator for https://github.com/pytorch/pytorch/issues/38349
I'm not entirely sure the changes to `NamedRegistrations.cpp` were needed but I saw other operators in there so I added it.
Normally the ldexp operator is used along with the frexp to construct and deconstruct floating point values. This is useful for performing operations on either the mantissa and exponent portions of floating point values.
Sleef, std math.h, and cuda support both ldexp and frexp but not for all data types. I wasn't able to figure out how to get the iterators to play nicely with a vectorized kernel so I have left this with just the normal CPU kernel for now.
This is the first operator I'm adding so please review with an eye for errors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45370
Reviewed By: mruberry
Differential Revision: D24333516
Pulled By: ranman
fbshipit-source-id: 2df78088f00aa9789aae1124eda399771e120d3f
Summary:
Fixes https://github.com/pytorch/pytorch/issues/47313
This PR implements `torch.addr` function using `TensorIterator` with `cpu_kernel_vec` and `gpu_kernel`.
It helps reduce memory usage, improve performance, and fix the bug when `beta` or `alpha` is a complex number.
Todo
- [x] benchmarking `torch.addr` for the change of this PR, as well as the legacy TH implementation used in PyTorch 1.6.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/47664
Reviewed By: zhangguanheng66
Differential Revision: D25059693
Pulled By: ngimel
fbshipit-source-id: 20a90824aa4cb2240e81a9f17a9e2f16ae6e3437
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
