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
Summary:
This PR performs a renaming of the function `potrf` responsible for the Cholesky
decomposition on positive definite matrices to `cholesky` as NumPy and TF do.
Billing of changes
- make potrf cname for cholesky in Declarations.cwrap
- modify the function names in ATen/core
- modify the function names in Python frontend
- issue warnings when potrf is called to notify users of the change
Reviewed By: soumith
Differential Revision: D10528361
Pulled By: zou3519
fbshipit-source-id: 19d9bcf8ffb38def698ae5acf30743884dda0d88
Summary:
Here is my stab at ```dense.to_sparse```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12171
Differential Revision: D10859078
Pulled By: weiyangfb
fbshipit-source-id: 5df72f72ba4f8f10e283402ff7731fd535682664
Summary:
include atomicAdd commentary as this is less well known
There is some discussion in #12207
Unfortunately, I cannot seem to get the ..include working in `_tensor_docs.py` and `_torch_docs.py`. I could use a hint for that.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12217
Differential Revision: D10419739
Pulled By: SsnL
fbshipit-source-id: eecd04fb7486bd9c6ee64cd34859d61a0a97ec4e
Summary:
Couple questions:
1) I used the log1p implementation in #8969 as a guide especially for testing. I'm not sure what the ```skipIfROCM``` annotation is for, so unsure if i need it for my test.
2) I implemented the branching logic in the narrow function itself; is this the right place to do so? I noticed that there a number of places where sparse-specific logic is handled with just an if statement in this file. Or should I implement a separate dispatch in native_functions.yml as in the log1p?
And of course, happy to make any any other updates/changes that I may have missed as well. This is my first PR to the project.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11342
Differential Revision: D9978430
Pulled By: weiyangfb
fbshipit-source-id: e73dc20302ab58925afb19e609e31f4a38c634ad
Summary:
- fix PR https://github.com/pytorch/pytorch/pull/11061 by moving `detach_()` and `set_requires_grad()` to `torch.tensor_ctor()` and `tensor.new_tensor`, and also removed warnings and `args_requires_grad` from `internal_new_from_data `
- with this patch, the returned tensor from `tensor_ctor()` and `new_tensor` will be detached from source tensor, and set requires_grad based on the input args
- `torch.as_tensor` retains its behavior as documented
gchanan apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11815
Differential Revision: D9932713
Pulled By: weiyangfb
fbshipit-source-id: 4290cbc57bd449954faadc597c24169a7b2d8259
Summary:
+ https://github.com/pytorch/pytorch/issues/10236 : torch.bernoulli's out kwarg is broken
fixed in moving `bernoulli_out` to ATen
+ https://github.com/pytorch/pytorch/issues/9917 : BUG torch.bernoulli(p.expand(shape)) is broken
fixed in moving all `bernoulli` ops in ATen to use the modern apply utils methods
+ https://github.com/pytorch/pytorch/issues/10357 : torch.bernoulli inconsistent gpu/cpu results
fixed by adding CUDA asserts
In order to use `curand_uniform4`, I made some changes to `CUDAApplyUtils.cuh`. Specifically, I introduced an optional template parameter `int step` to the `CUDA_tensor_applyN` methods, representing that we want to process `step` values at each time for each of the `N` tensors.
The calling convention for `step = 1` (default) isn't changed. But if `step > 1`, the given lambda `op` must take in `int n` as its first argument, representing the number of valid values, because there may not be full `step` values at the boundary. E.g., here is what the `bernoulli(self, p_tensor)` call look like:
```cpp
// The template argument `4` below indicates that we want to operate on four
// element at each time. See NOTE [ CUDA_tensor_applyN helpers ] for details.
at::cuda::CUDA_tensor_apply2<scalar_t, prob_t, 4>(
ret, p,
[seeds] __device__(
int n, scalar_t& v1, scalar_t& v2, scalar_t& v3, scalar_t& v4,
const prob_t& p1, const prob_t& p2, const prob_t& p3, const prob_t& p4) {
curandStatePhilox4_32_10_t state;
curand_init(
seeds.first,
blockIdx.x * blockDim.x + threadIdx.x,
seeds.second,
&state);
float4 rand = curand_uniform4(&state);
switch (n) {
case 4: {
assert(0 <= p4 && p4 <= 1);
v4 = static_cast<scalar_t>(rand.w <= p4);
}
case 3: {
assert(0 <= p3 && p3 <= 1);
v3 = static_cast<scalar_t>(rand.z <= p3);
}
case 2: {
assert(0 <= p2 && p2 <= 1);
v2 = static_cast<scalar_t>(rand.y <= p2);
}
case 1: {
assert(0 <= p1 && p1 <= 1);
v1 = static_cast<scalar_t>(rand.x <= p1);
}
}
}
);
```
Benchmarking on `torch.rand(200, 300, 400)` 20 times, each time with 20 loops:
post patch
```
➜ ~ numactl --cpunodebind 1 --membind 1 -- taskset -c 12,13,14,15,16,17,18,19,20,21,22,23 env CUDA_LAUNCH_BLOCKING=1 python bern.py
torch.bernoulli(x)
6.841588497161865 +- 0.05413117632269859
torch.bernoulli(xc)
0.05963418632745743 +- 0.0008014909108169377
x.bernoulli_()
0.4024486541748047 +- 0.0021550932433456182
xc.bernoulli_()
0.02167394384741783 +- 2.3818030967959203e-05
```
pre-patch
```
➜ ~ numactl --cpunodebind 1 --membind 1 -- taskset -c 12,13,14,15,16,17,18,19,20,21,22,23 env CUDA_LAUNCH_BLOCKING=1 python bern.py
torch.bernoulli(x)
12.394511222839355 +- 0.0966421514749527
torch.bernoulli(xc)
0.08970972150564194 +- 0.0038722590543329716
x.bernoulli_()
1.654480218887329 +- 0.02364428900182247
xc.bernoulli_()
0.058352887630462646 +- 0.003094920190051198
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10273
Differential Revision: D9831294
Pulled By: SsnL
fbshipit-source-id: 65e0655a36b90d5278b675d35cb5327751604088
Summary:
vishwakftw Your patch needed some updates because the default native function dispatches changed from `[function, method]` to `[function]`. The CI was run before that change happened so it still shows green, but the internal test caught it.
I did some changes when rebasing and updating so I didn't just force push to your branch. Let's see if this passes CI and internal test. If it does, let me know if you want me to force push to your branch or use this PR instead.
Note to reviewers: patch was already approved at #10068 .
cc yf225
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11421
Differential Revision: D9733407
Pulled By: SsnL
fbshipit-source-id: cf2ed293bb9942dcc5158934ff4def2f63252599
Summary:
Test only for existence for now. I had to skip a lot of them so there a FIXME in the test.
Also I'm not testing torch.* because of namespace issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10311
Differential Revision: D9196341
Pulled By: SsnL
fbshipit-source-id: 9c2ca1ffe660bc1cc664474993f8a21198525ccc
Summary:
fixes#4176 cc vishwakftw
I didn't do `:math:` and `\neg` because I am using double ticks so they render more similarly with `:attr:`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9630
Differential Revision: D8933022
Pulled By: SsnL
fbshipit-source-id: 31d8551f415b624c2ff66b25d886f20789846508
