Summary:
With the CI failure caused in 8bbafa0b32 fixed (incorrect return type of the lambdas in CUDA kernels)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/30521
Differential Revision: D18770151
Pulled By: ailzhang
fbshipit-source-id: 02f0fe1d5718c34d24da6dbb5884ee8b247ce39a
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27782
Warnings show up when running `make html` to build documentation. All of
the warnings are very reasonable and point to bugs in our docs. This PR
attempts to fix most of those warnings.
In the future we will add something to the CI that asserts that there
are no warnings in our docs.
Test Plan: - build and view changes locally
Differential Revision: D17887067
Pulled By: zou3519
fbshipit-source-id: 6bf4d08764759133b20983d6cd7f5d27e5ee3166
Summary:
Added Complex support with AVX to unary ops and binary ops.
I need to add nan propagation to minimum() and maximum() in the future.
In-tree changes to pytorch to support complex numbers are being submitted here.
Out-of-tree support for complex numbers is here: pytorch-cpu-strided-complex extension
Preliminary Benchmarks are here.
I tried rrii and riri and found that riri is better in most situations.
Divide is very slow because you can't reduce 1/(x+y)
Sqrt is also very slow.
Reciprocal could be sped up after I add conj()
Everything else is typically within 20% of the real number performance.
Questions:
Why does macOS not support mil? #if AT_MKL_ENABLED() && !defined(__APPLE__) in vml.h. MKL does support some complex operations like Abs, so I was curious about trying it.
Is MKL just calling AVX?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26500
Differential Revision: D17835431
Pulled By: ezyang
fbshipit-source-id: 6746209168fbeb567af340c22bf34af28286bd54
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/27173
`docs/source/named_tensor.rst` is the entry point; most users will land
either here or the named tensor tutorial when looking to use named
tensors. We should strive to make this as readable, concise, and understandable
as possible.
`docs/source/name_inference.rst` lists all of the name inference rules.
It should be clear but it's hard to make it concise.
Please let me know if anything doesn't make sense and please propose
alternative wordings and/or restructuring to improve the documentation.
This should ultimately get cherry-picked into the 1.3 branch as one
monolithic commit so it would be good to get all necessary changes made
in this PR and not have any follow ups.
Test Plan: - built and reviewed locally with `cd docs/ && make html`.
Differential Revision: D17763046
Pulled By: zou3519
fbshipit-source-id: c7872184fc4b189d405b18dad77cad6899ae1522
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26675
Based on offline poll, we're very unlikely to have multi-axis quantized tensors in the foreseeable future. Let's simplify API and just return int instead of list. It also matches the singular `axis` name.
Test Plan: Imported from OSS
Differential Revision: D17537052
Pulled By: dzhulgakov
fbshipit-source-id: 676abc3b251d288468aaed467b5e5ca4063b98b0
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/26240
In particular adds support for empty/empty_like which is needed for memory layouts to work.
Test Plan: Imported from OSS
Differential Revision: D17443220
Pulled By: dzhulgakov
fbshipit-source-id: 9c9e25981999c0edaf40be104a5741e9c62a1333
Summary:
This patch writes documentation for `Tensor.record_stream()`, which is not a documented API currently. I've discussed publishing it with colesbury in https://github.com/pytorch/pytorch/issues/23729.
The documentation is based on [the introduction at `CUDACachingAllocator.cpp`](25d1496d58/c10/cuda/CUDACachingAllocator.cpp (L47-L50)). ~~I didn't explain full details of the life cycle of memory blocks or stream awareness of the allocator for the consistent level of details with other documentations.~~ I explained about the stream awareness in a note block.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24078
Differential Revision: D16743526
Pulled By: zou3519
fbshipit-source-id: 05819c3cc96733e2ba93c0a7c0ca06933acb22f3
Summary:
Changelog:
- Rename `gels` to `lstsq`
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lstsq` under the name `gels` and add a deprecation warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23460
Test Plan: - All tests should pass to confirm that the patch is correct
Differential Revision: D16547834
Pulled By: colesbury
fbshipit-source-id: b3bdb8f4c5d14c7716c3d9528e40324cc544e496
Summary:
Something flaky is going on with `test_inplace_view_saved_output` on Windows.
With my PR #20598 applied, the test fails, even though there is no obvious reason it should be related, so the PR was reverted.
Based on commenting out various parts of my change and re-building, I think the problem is with the name -- renaming everything from `T` to `asdf` seems to make the test stop failing. I can't be sure that this is actually the case though, since I could just be seeing patterns in non-deterministic build output...
I spoke with colesbury offline and we agreed that it is okay to just disable this test on Windows for now and not block landing the main change. He will look into why it is failing.
**Test Plan:** I will wait to make sure the Windows CI suite passes before landing this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21175
Differential Revision: D15566970
Pulled By: umanwizard
fbshipit-source-id: edf223375d41faaab0a3a14dca50841f08030da3
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21196
we'll add `quantize(quantizer)` as a tensor method later when we expose `quantizer` in Python frontend
Python
```
torch.quantize_linear(t, ...)
```
C++
```
at::quantize_linear(t, ...)
```
Differential Revision: D15577123
fbshipit-source-id: d0abeea488418fa9ab212f84b0b97ee237124240
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21156
we'll add `quantize(quantizer)` as a tensor method later when we expose `quantizer` in Python frontend
Python
```
torch.quantize_linear(t, ...)
```
C++
```
at::quantize_linear(t, ...)
```
Differential Revision: D15558784
fbshipit-source-id: 0b194750c423f51ad1ad5e9387a12b4d58d969a9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20874
A criteria for what should go in Tensor method is whether numpy has it, for this one it does not
so we are removing it as a Tensor method, we can still call it as function.
Python
```
torch.quantize_linear(t, ...), torch.dequantize(t)
```
C++
```
at::quantize_linear(t, ...), at::dequantize(t)
```
Reviewed By: dzhulgakov
Differential Revision: D15477933
fbshipit-source-id: c8aa81f681e02f038d72e44f0c700632f1af8437
Summary:
This PR covers two important points with respect to the QR decomposition:
- batching of input matrices (#7500)
- adding `some` as an option in `torch.qr` akin to NumPy's `mode` option (#10538)
Changelog:
- Enable batching for inputs to `torch.qr`
- Move QR decomposition implementation to ATen (CPU and CUDA)
- Remove existing implementations in TH/THC
- Add a `some` option to `torch.qr` that will enable users to switch between complete and reduced decomposition
- Modify doc strings
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20689
Differential Revision: D15529230
Pulled By: soumith
fbshipit-source-id: 16af82b1d2db8a3a758fa8a5f798d83f5f950efb
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20938
Dequantize_linear need not be exposed to the front end users.
It will only be used for the jit passes for q-dq insertion and op
substitution.
Differential Revision: D15446097
fbshipit-source-id: a5fbcf2bb72115122c9653e5089d014e2a2e891d
Summary:
Changelog:
- Rename `potri` to `cholesky_inverse` to remain consistent with names of `cholesky` methods (`cholesky`, `cholesky_solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `cholesky_inverse` under the name `potri` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19498
Differential Revision: D15029901
Pulled By: ezyang
fbshipit-source-id: 2074286dc93d8744cdc9a45d54644fe57df3a57a
Summary:
Make it possible to construct a pinned memory tensor without creating a storage first and without calling pin_memory() function. It is also faster, as copy operation is unnecessary.
Supported functions:
```python
torch.rand_like(t, pin_memory=True)
torch.randn_like(t, pin_memory=True)
torch.empty_like(t, pin_memory=True)
torch.full_like(t, 4, pin_memory=True)
torch.zeros_like(t, pin_memory=True)
torch.ones_like(t, pin_memory=True)
torch.tensor([10,11], pin_memory=True)
torch.randn(3, 5, pin_memory=True)
torch.rand(3, pin_memory=True)
torch.zeros(3, pin_memory=True)
torch.randperm(3, pin_memory=True)
torch.empty(6, pin_memory=True)
torch.ones(6, pin_memory=True)
torch.eye(6, pin_memory=True)
torch.arange(3, 5, pin_memory=True)
```
Part of the bigger: `Remove Storage` plan.
Now compatible with both torch scripts:
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"), pin_memory=False)`
and
` _1 = torch.zeros([10], dtype=6, layout=0, device=torch.device("cpu"))`
Same checked for all similar functions `rand_like`, `empty_like` and others
It is fixed version of #18455
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18952
Differential Revision: D14801792
Pulled By: VitalyFedyunin
fbshipit-source-id: 8dbc61078ff7a637d0ecdb95d4e98f704d5450ba
