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
Summary:
Changelog:
- Rename `btrisolve` to `lu_solve` to remain consistent with names of solve methods (`cholesky_solve`, `triangular_solve`, `solve`)
- Fix all callsites
- Rename all tests
- Create a tentative alias for `lu_solve` under the name `btrisolve` and add a deprecation warning to not promote usage
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18726
Differential Revision: D14726237
Pulled By: zou3519
fbshipit-source-id: bf25f6c79062183a4153015e0ec7ebab2c8b986b
Summary:
This is a minimalist PR to add MKL-DNN tensor per discussion from Github issue: https://github.com/pytorch/pytorch/issues/16038
Ops with MKL-DNN tensor will be supported in following-up PRs to speed up imperative path.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17748
Reviewed By: dzhulgakov
Differential Revision: D14614640
Pulled By: bddppq
fbshipit-source-id: c58de98e244b0c63ae11e10d752a8e8ed920c533
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18230
Implementing minimum qtensor API to unblock other workstreams in quantization
Changes:
- Added Quantizer which represents different quantization schemes
- Added qint8 as a data type for QTensor
- Added a new ScalarType QInt8
- Added QTensorImpl for QTensor
- Added following user facing APIs
- quantize_linear(scale, zero_point)
- dequantize()
- q_scale()
- q_zero_point()
Reviewed By: dzhulgakov
Differential Revision: D14524641
fbshipit-source-id: c1c0ae0978fb500d47cdb23fb15b747773429e6c
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.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18455
Reviewed By: ezyang
Differential Revision: D14672084
Pulled By: VitalyFedyunin
fbshipit-source-id: 9d0997ec00f59500ee018f8b851934d334012124
Summary:
Changelog:
- Renames `btrifact` and `btrifact_with_info` to `lu`to remain consistent with other factorization methods (`qr` and `svd`).
- Now, we will only have one function and methods named `lu`, which performs `lu` decomposition. This function takes a get_infos kwarg, which when set to True includes a infos tensor in the tuple.
- Rename all tests, fix callsites
- Create a tentative alias for `lu` under the name `btrifact` and `btrifact_with_info`, and add a deprecation warning to not promote usage.
- Add the single batch version for `lu` so that users don't have to unsqueeze and squeeze for a single square matrix (see changes in determinant computation in `LinearAlgebra.cpp`)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18435
Differential Revision: D14680352
Pulled By: soumith
fbshipit-source-id: af58dfc11fa53d9e8e0318c720beaf5502978cd8
Summary:
This depend on https://github.com/pytorch/pytorch/pull/16039
This prevent people (reviewer, PR author) from forgetting adding things to `tensors.rst`.
When something new is added to `_tensor_doc.py` or `tensor.py` but intentionally not in `tensors.rst`, people should manually whitelist it in `test_docs_coverage.py`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16057
Differential Revision: D14619550
Pulled By: ezyang
fbshipit-source-id: e1c6dd6761142e2e48ec499e118df399e3949fcc
Summary:
Changelog:
- Renames `trtrs` to `triangular_solve` to remain consistent with `cholesky_solve` and `solve`.
- Rename all tests, fix callsites
- Create a tentative alias for `triangular_solve` under the name `trtrs`, and add a deprecation warning to not promote usage.
- Move `isnan` to _torch_docs.py
- Remove unnecessary imports
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18213
Differential Revision: D14566902
Pulled By: ezyang
fbshipit-source-id: 544f57c29477df391bacd5de700bed1add456d3f
Summary:
Why do we need this workaround? `PythonArgParser` handles these two cases well.
The discussion started at https://github.com/pytorch/pytorch/pull/6201#issuecomment-378724406. The conclusion at that time by goldsborough was:
> Because we wanted to allow `dim=None` in Python and route to a different function. Essentially the problem was wanting to wrap the C++ function in Python. AFAIK there is no way of translating `dim=None` behavior into C++? So Richard and I came up with this strategy
Maybe at that time `PythonArgParser` was not powerful enough to handle the routing of two function with same name but different C++ signature.
Will keep an eye on the CI.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17103
Differential Revision: D14523503
Pulled By: VitalyFedyunin
fbshipit-source-id: cae3e2678062da2eccd93b51d4050578c7a9ab80
Summary:
Changelog:
- Renames `gesv` to `solve` to remain consistent with `cholesky_solve`.
- Rename all tests, fix callsites
- Create a tentative alias for `solve` under the name `gesv`, and add a deprecated warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18060
Differential Revision: D14503117
Pulled By: zou3519
fbshipit-source-id: 99c16d94e5970a19d7584b5915f051c030d49ff5
Summary:
Eventually we should remove these when we're certain that all our ops
handle memory overlaps correctly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17576
Differential Revision: D14349990
Pulled By: zou3519
fbshipit-source-id: c3a09f6113b9b1bf93e7f13c0b426c45b2cdf21f
Summary: Update the docs to include the value parameter that was missing in the `scatter_` function.
Differential Revision: D14209225
Pulled By: soumith
fbshipit-source-id: 5c65e4d8fbd93fcd11a0a47605bce6d57570f248
Summary: The `tensor` be used as `end` clarified in the docs.
Differential Revision: D14132212
Pulled By: ezyang
fbshipit-source-id: e9bca14d5079e5f7adfc18afcb1eec832ef86e9e
Summary:
Based on https://github.com/pytorch/pytorch/pull/12413, with the following additional changes:
- Inside `native_functions.yml` move those outplace operators right next to everyone's corresponding inplace operators for convenience of checking if they match when reviewing
- `matches_jit_signature: True` for them
- Add missing `scatter` with Scalar source
- Add missing `masked_fill` and `index_fill` with Tensor source.
- Add missing test for `scatter` with Scalar source
- Add missing test for `masked_fill` and `index_fill` with Tensor source by checking the gradient w.r.t source
- Add missing docs to `tensor.rst`
Differential Revision: D14069925
Pulled By: ezyang
fbshipit-source-id: bb3f0cb51cf6b756788dc4955667fead6e8796e5
Summary:
This PR is a simple fix for the mistake in the "tensor" and "torch.Tensor"doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16842
Differential Revision: D14020300
Pulled By: ezyang
fbshipit-source-id: 3ab04f1223d6e60f8da578d04d759e385d23acbb
Summary:
So that things like below can be JITable, and available in C++ API:
```python
import torch
torch.jit.script
def f(x, y, z):
x.index_add(0, y, z)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12413
Differential Revision: D13899948
Pulled By: suo
fbshipit-source-id: b0006b4bee2d1085c813733e1037e2dcde4ce626
Summary:
Changelog:
- Renames `potrs` to `cholesky_solve` to remain consistent with Tensorflow and Scipy (not really, they call their function chol_solve)
- Default argument for upper in cholesky_solve is False. This will allow a seamless interface between `cholesky` and `cholesky_solve`, since the `upper` argument in both function are the same.
- Rename all tests
- Create a tentative alias for `cholesky_solve` under the name `potrs`, and add deprecated warning to not promote usage.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15334
Differential Revision: D13507724
Pulled By: soumith
fbshipit-source-id: b826996541e49d2e2bcd061b72a38c39450c76d0
Summary:
This PR enables autodiff to use the forward/backward graph compiled from python code, instead of using symbolic gradients(modifying the original graph directly).
We put the map in a separate .h file for now to wait for the native_functions.yaml and derivatives.yaml merge. This should ideally go into native_functions.yaml eventually.
This PR should be enough to unblock us for now, we can start writing gradients for aten functions in python.
Differential Revision: D13494635
Pulled By: ailzhang
fbshipit-source-id: f8d51a15243ac46afd09d930c573ccdfcd9fdaaf
Summary:
```
The most significant change is that this fixes the error message when
indexing an empty tensor with an out-of-bounds index. For example:
x = torch.ones(10, 0)
x[:, [3, 4]]
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14441
Differential Revision: D13226737
Pulled By: colesbury
fbshipit-source-id: d1c4a35a30e3217e3d1727d13f6b354a4a3b2a24
Summary:
Add to the Tensor doc info about `.device`, `.is_cuda`, `.requires_grad`, `.is_leaf` and `.grad`.
Update the `register_backward_hook` doc with a warning stating that it does not work in all cases.
Add support in the `_add_docstr` function to add docstring to attributes.
There is an explicit cast here but I am not sure how to handle it properly. The thing is that the doc field for getsetdescr is written as being a const char * (as all other doc fields in descriptors objects) in cpython online documentation. But in the code, it is the only one that is not const.
I assumed here that it is a bug in the code because it does not follow the doc and the convention of the others descriptors and so I cast out the const.
EDIT: the online doc I was looking at is for 3.7 and in that version both the code and the doc are const. For older versions, both are non const.
Please let me know if this should not be done. And if it should be done if there is a cleaner way to do it !
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14339
Differential Revision: D13243266
Pulled By: ezyang
fbshipit-source-id: 75b7838f7cd6c8dc72b0c61950e7a971baefaeeb
