Summary:
- This is a straightforward PR, building up on the batch inverse PR, except for one change:
- The GENERATE_LINALG_HELPER_n_ARGS macro has been removed, since it is not very general and the resulting code is actually not very copy-pasty.
Billing of changes:
- Add batching for `potrs`
- Add relevant tests
- Modify doc string
Minor changes:
- Remove `_gesv_single`, `_getri_single` from `aten_interned_strings.h`.
- Add test for CUDA `potrs` (2D Tensor op)
- Move the batched shape checking to `LinearAlgebraUtils.h`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13453
Reviewed By: soumith
Differential Revision: D12942039
Pulled By: zou3519
fbshipit-source-id: 1b8007f00218e61593fc415865b51c1dac0b6a35
Summary:
update roll to behave as in numpy.roll when dimension to roll not specified.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13588
Differential Revision: D12964295
Pulled By: nairbv
fbshipit-source-id: de9cdea1a937773033f081f8c1505a40e4e08bc1
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:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12761
, is not really , and thus it can fail some of the Python 2 import.
Reviewed By: weiyangfb
Differential Revision: D10423231
fbshipit-source-id: 3738c0b9d2f52aa47eef06250f84c5933a38783f
Summary:
Fixes#12251
In the docs the actual key word argument was supposed to be `tensors` but instead it is given as `seq` for doing `torch.cat` operation.
zou3519 can you review this code? I don't have access to request for code reviews.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12741
Differential Revision: D10419682
Pulled By: ezyang
fbshipit-source-id: a0ec9c3f4aeba23ac3a99e2ae89bd07d2b9ddb58
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:
- Fix broken sparse_coo_examples, update output
- Tensor(...) to tensor(...)
- Fix arguments to math.log to be floats
While the last might be debateable, mypy currently complains when passing an int to math.log. As it is not essential for our examples, let's be clean w.r.t. other people's expectations.
These popped up while checking examples in the context of #12500 .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12707
Differential Revision: D10415256
Pulled By: SsnL
fbshipit-source-id: c907b576b02cb0f89d8f261173dbf4b3175b4b8d
Summary:
Add dtype argument to softmax/log_softmax functions.
Computing softmax in fp32 precision is necessary for mixed precision training, and converting output of the previous layer into fp32 and then reading it as fp32 in softmax is expensive, memory and perf-wise, this PR allows one to avoid it.
For most input data/dtype combinations, input data is converted to dtype and then softmax is computed. If input data is half type and dtype is fp32, kernels with the corresponding template arguments are called.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11719
Reviewed By: ezyang
Differential Revision: D10175514
Pulled By: zou3519
fbshipit-source-id: 06d285af91a0b659932236d41ad63b787eeed243
Summary:
- fix https://github.com/pytorch/pytorch/issues/12120
- add `torch.argsort`, `torch.pdist`, `broadcast_tensors` to *.rst files
- add parameter dim to `torch.unique` doc
- fix table and args for `torch.norm`
- test plan: make html and check docs in browser
gchanan
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12126
Differential Revision: D10087006
Pulled By: weiyangfb
fbshipit-source-id: 25f65c43d14e02140d0da988d8742c7ade3d8cc9
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:
Adds vararg support for meshgrid and adds checks for all the tensor arguments to have the same dtype and device.
Fixes: [#10823](https://github.com/pytorch/pytorch/issues/10823), #11446
The earlier pull request closed without any changes because I had some rebasing issues, so I made another pull request to close out #10823. Sorry for the inconvenience.
Differential Revision: D9892876
Pulled By: ezyang
fbshipit-source-id: 93d96cafc876102ccbad3ca2cc3d81cb4c9bf556
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:
Fix#10345, which only happens in CUDA case.
* Instead of returning some random buffer, we fill it with zeros.
* update torch.symeig doc.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10645
Reviewed By: soumith
Differential Revision: D9395762
Pulled By: ailzhang
fbshipit-source-id: 0f3ed9bb6a919a9c1a4b8eb45188f65a68bfa9ba
Summary:
Implemented via a wrapper, thank you Richard for the suggestion!
Fixes: #9929
Pull Request resolved: https://github.com/pytorch/pytorch/pull/10067
Differential Revision: D9083388
Pulled By: soumith
fbshipit-source-id: 9ab21cd35278b01962e11d3e70781829bf4a36da
Summary:
A 0-dimensional tensor is now returned when squeezing a tensor with a single element.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9529
Differential Revision: D8893103
Pulled By: soumith
fbshipit-source-id: 658189ecfff283b2b7281feb16a397692d6dbd8f
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9497Fixes#7883 by using `rfft`.
It's worth noting that this is BC breaking. And it's impossible to detect the change because the two signatures before and after this change supports a common subset of calling patterns, e.g., `stft(Tensor, int, int)`. (some other calling patterns will raise error).
soumith and I plan to change the current `stft` interface because it is a bit messy and non-standard. rafaelvalle suggested us that `librosa` is a good reference API to align with. After discussing with soumith and ezyang , and given that `stft` is only out for 1 release, I decide to go with directly changing the signature. Also, my understanding is that most researchers in this field will welcome this change as `librosa` seems to be the golden-standard here. (it doesn't yet support all `pad_mode` but those will become available if added to `F.pad`.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9308
Reviewed By: ezyang
Differential Revision: D8806148
Pulled By: SsnL
fbshipit-source-id: f6e8777d0c34d4a4d7024e638dc9c63242e8bb58
