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Author SHA1 Message Date
Edward Yang
2ed99fee0d Revert D13935403: Call c10 cuda op from test_torch 
Differential Revision:
D13935403

Original commit changeset: b2915ec8a366

fbshipit-source-id: 0f3409d5c102d719bc1f0483695aee93e7d613c9
 2019-03-01 14:18:26 -08:00
Sebastian Messmer
0a7b2af13b Call c10 cuda op from test_torch 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16692

Reviewed By: ezyang

Differential Revision: D13935403

fbshipit-source-id: b2915ec8a3664bb6e918ed357908cc33d8f9449a
 2019-03-01 10:59:19 -08:00
bhushan
a6170573c8 Adding support for 0-d tensor for transpose (.t()) (#17535) 
Summary:
- Test updates
1. test_torch: added 0-d test case and t_() test cases
2. test_jit  : updated error message for TestAsync.test_async_script_error

- Updating documentation for torch.t()
Adding information regarding new support of 0-D and 1-D tenso

Fixes #17520
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17535

Differential Revision: D14269984

Pulled By: gchanan

fbshipit-source-id: 38b723f31484be939261c88edb33575d242eca65
 2019-03-01 08:45:01 -08:00
Xiang Gao
2e5a8cee82 Customize the printing of namedtuple return (#17136) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/17112
```python
print("good", torch.randn(5,5,5).max(1))
print("terrible", torch.randn(5,5,10).max(1))
print("not as good", torch.randn(5,5,500).max(1))
print ("old behaviour = gold standard")
print(tuple(torch.randn(5,5,5).max(1)))
print(tuple(torch.randn(5,5,10).max(1)))
print(tuple(torch.randn(5,5,500).max(1)))
```
now gives
```
>>> import torch
>>> print("good", torch.randn(5,5,5).max(1))
good torch.return_types.max(
values=tensor([[ 1.2821,  1.8063,  1.8075,  1.3082, -0.1267],
        [ 0.3437,  0.7353,  1.2619,  0.7557,  1.6662],
        [ 0.8583,  1.8906,  1.0246,  1.7598,  1.1184],
        [ 1.7821,  0.0230,  0.9452,  1.0318,  1.0823],
        [ 0.4116, -0.0379, -0.1843,  1.4129,  1.8796]]),
indices=tensor([[4, 4, 3, 2, 1],
        [1, 2, 4, 1, 1],
        [2, 4, 0, 2, 1],
        [0, 2, 0, 3, 1],
        [0, 4, 4, 4, 4]]))
>>> print("terrible", torch.randn(5,5,10).max(1))
terrible torch.return_types.max(
values=tensor([[ 2.1272,  1.3664,  2.2067,  1.3974, -0.0883,  1.2505,  1.0074,  1.1217,
          0.3849,  0.6936],
        [ 0.6288, -0.4560,  1.2748,  1.5482,  1.2777,  1.6874,  0.7151,  0.6041,
          1.3572,  1.6232],
        [ 1.6703,  1.0075,  1.6480,  2.2839,  1.3390,  0.4938,  1.6449,  1.7628,
          0.8141,  2.5714],
        [ 0.7079,  1.8677,  3.2478,  1.5591,  2.4870,  0.8635, -0.1450,  1.6923,
          1.4924,  1.6298],
        [ 2.4056,  0.8002,  0.9317,  0.7455,  0.7866,  2.1191,  0.3492,  1.2095,
          1.8637,  1.7470]]),
indices=tensor([[1, 1, 0, 0, 0, 0, 3, 4, 4, 4],
        [4, 2, 2, 1, 2, 2, 3, 1, 1, 3],
        [0, 3, 3, 0, 2, 1, 4, 1, 0, 1],
        [4, 1, 3, 0, 3, 2, 0, 1, 4, 3],
        [1, 0, 3, 2, 1, 0, 0, 1, 0, 1]]))
>>> print("not as good", torch.randn(5,5,500).max(1))
not as good torch.return_types.max(
values=tensor([[ 0.3877,  0.7873,  1.8701,  ...,  0.5971,  1.6103, -0.3435],
        [ 1.1300,  2.2418,  1.4239,  ...,  1.3943,  0.3872,  1.6475],
        [ 2.0656,  1.3136,  0.9896,  ...,  2.3918,  0.8226,  1.0517],
        [ 1.1054,  0.9945,  1.0561,  ...,  2.1039,  1.1524,  3.0304],
        [ 1.5041,  2.2809,  1.0883,  ...,  0.8504,  2.4774,  1.1041]]),
indices=tensor([[4, 3, 1,  ..., 1, 4, 0],
        [4, 4, 4,  ..., 3, 0, 3],
        [3, 0, 1,  ..., 2, 2, 4],
        [0, 1, 1,  ..., 4, 2, 2],
        [1, 0, 4,  ..., 2, 0, 2]]))
>>> print ("old behaviour = gold standard")
old behaviour = gold standard
>>> print(tuple(torch.randn(5,5,5).max(1)))
(tensor([[ 1.1908,  1.1807,  1.3151,  1.7184,  0.3556],
        [ 0.3798,  0.9213,  0.3001,  1.3087,  2.2419],
        [ 1.4233,  1.4814,  1.9900,  1.7744,  1.3059],
        [ 1.0026, -0.0330,  1.3061,  1.8730,  2.0685],
        [ 1.3041,  1.6458,  1.3449,  1.8948,  3.6206]]), tensor([[0, 4, 3, 4, 0],
        [1, 1, 4, 0, 4],
        [4, 1, 0, 3, 3],
        [1, 2, 1, 4, 0],
        [3, 3, 0, 3, 3]]))
>>> print(tuple(torch.randn(5,5,10).max(1)))
(tensor([[-0.1232,  0.8275,  0.6732,  1.1223,  0.8247,  1.2851,  1.6009,  1.9979,
          1.9109,  0.7313],
        [ 0.2260,  0.5922,  1.6928,  0.6024,  2.1158,  3.0619,  0.5653,  0.7426,
          0.8316,  0.6346],
        [ 0.4319,  0.2231,  0.5255,  1.7620,  1.1657,  0.8875,  0.5782,  0.6506,
          0.5032,  1.7097],
        [ 0.4137,  1.7265,  1.4260,  2.0301,  1.2244,  0.7128,  2.6345,  0.7230,
          1.3553,  1.6508],
        [ 1.0684,  1.7195,  1.4068,  0.7076, -0.0242,  0.8474,  0.8754,  1.7108,
          0.2188,  1.1584]]), tensor([[0, 1, 3, 4, 2, 3, 4, 2, 1, 0],
        [1, 4, 0, 0, 3, 2, 0, 0, 3, 3],
        [2, 3, 1, 1, 4, 0, 1, 4, 4, 4],
        [0, 4, 1, 3, 2, 0, 2, 0, 3, 1],
        [1, 0, 0, 0, 0, 3, 3, 3, 2, 0]]))
>>> print(tuple(torch.randn(5,5,500).max(1)))
(tensor([[0.9395, 1.5572, 1.8797,  ..., 2.0494, 0.8202, 0.9623],
        [1.7937, 0.7225, 1.8836,  ..., 0.7927, 1.4976, 1.1813],
        [0.8558, 1.6943, 1.4192,  ..., 0.8327, 1.9661, 0.4197],
        [1.2993, 1.4995, 0.9357,  ..., 0.7810, 1.3030, 2.6216],
        [1.4206, 1.8315, 1.0338,  ..., 1.4312, 1.3198, 1.5233]]), tensor([[0, 4, 3,  ..., 3, 0, 2],
        [0, 1, 0,  ..., 0, 4, 3],
        [3, 4, 3,  ..., 3, 0, 0],
        [3, 2, 3,  ..., 1, 2, 1],
        [1, 2, 4,  ..., 3, 1, 3]]))
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17136

Differential Revision: D14250021

Pulled By: VitalyFedyunin

fbshipit-source-id: aae72f03b35980063b1ac1f07b8353eddb0c8b93
 2019-02-28 13:07:26 -08:00
bhushan
4ca1a54526 Make transpose consistent with numpy's behavior (#17462) 
Summary:
Pytorch's tensor.t() is now equivalent with Numpy's ndarray.T for 1D tensor
i.e. tensor.t() == tensor

Test case added:
- test_t

fixes #9687
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17462

Differential Revision: D14214838

Pulled By: soumith

fbshipit-source-id: c5df1ecc8837be22478e3a82ce4854ccabb35765
 2019-02-26 14:23:19 -08:00
Stefan Krah
e4e9b738d3 Followup to #17049: change more instances of RuntimeError to IndexError 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17114

Differential Revision: D14150890

Pulled By: gchanan

fbshipit-source-id: 579ca71665166c6a904b894598a0b334f0d8acc7
 2019-02-25 15:34:22 -08:00
Gregory Chanan
15a55b86ed Fix nonzero for scalars on cuda, to_sparse for scalars on cpu/cuda. (#17406) 
Summary:
I originally set out to fix to_sparse for scalars, which had some overly restrictive checking (sparse_dim > 0, which is impossible for a scalar).

This fix uncovered an issue with nonzero: it didn't properly return a size (z, 0) tensor for an input scalar, where z is the number of nonzero elements (i.e. 0 or 1).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17406

Differential Revision: D14185393

Pulled By: gchanan

fbshipit-source-id: f37a6e1e3773fd9cbf69eeca7fdebb3caa192a19
 2019-02-25 08:23:40 -08:00
Xiang Gao
b2dde4386a Namedtuple return for symeig, eig, pstrf, qr, geqrf (#16950) 
Summary: More ops for https://github.com/pytorch/pytorch/issues/394

Differential Revision: D14118645

Pulled By: ezyang

fbshipit-source-id: a98646c3ddcbe4e34452aa044951286dcf9df778
 2019-02-20 14:01:19 -08:00
SsnL
79f898263b Improve error message w/ size inference on empty tensors 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17255

Differential Revision: D14143094

Pulled By: soumith

fbshipit-source-id: f96fa7f8eb6eaac72887d3e837546cbfa505f101
 2019-02-20 09:12:26 -08:00
Will Feng
c88798dbc1 Make tril_ and triu_ actually in-place (#17031) 
Summary:
Currently, when the input tensor `self` is not contiguous, `tril_` and `triu_` calls `self = self.contiguous()`, which allocates a new contiguous tensor and assign it to `self`. This effectively changes the input tensor `self`'s pointer and will break downstream code after Variable/Tensor merge.

This PR fixes it so that `tril_` and `triu_` always update the input tensor in-place and preserve the input tensor's TensorImpl.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17031

Differential Revision: D14069592

Pulled By: yf225

fbshipit-source-id: d188218f426446a44ccc1d33fc28ac3f828c6a05
 2019-02-19 14:47:17 -08:00
Iurii Zdebskyi
444039c47b Bool tensor. Part 0: Boolean storage implementation (#16810) 
Summary:
This is the first commit from a series of planned changes in order to add boolean tensors to PyTorch. The whole plan looks like this:

0. Storage Implementation (this change)
1. Tensor Creation.
2. Tensor Conversions.
3. Tensor Indexing.
4. Tensor Operations.
5. Back compatibility related changes.

This feature was requested by the community:
https://github.com/pytorch/pytorch/issues/4764
https://github.com/pytorch/pytorch/issues/4219
https://github.com/pytorch/pytorch/issues/4288

**Change**:
Added boolean type to the Storage class for CPU and CUDA backends.

**Tested via**:
1. unit tests
2. running this:
-> import torch
-> torch.BoolStorage
<class 'torch.BoolStorage'>
-> torch.cuda.BoolStorage
<class 'torch.cuda.BoolStorage'>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16810

Reviewed By: gchanan

Differential Revision: D14087246

Pulled By: izdeby

fbshipit-source-id: 042642ced1cb0fd1bb6bff05f9ca871a5c54ee5e
 2019-02-19 08:22:13 -08:00
Gao, Xiang
b6b99fd7d3 Add namedtuple return for min, median, mode, kthvalue, add test for namedtuple return API (#16186) 
Summary:
This partially fixes https://github.com/pytorch/pytorch/issues/394 and depend on https://github.com/pytorch/pytorch/pull/15429. I suggest to review this only after https://github.com/pytorch/pytorch/pull/15429 get landed, otherwise the diff might be large to review.

The test only allows explicitly whitelisted operators to have named return.

Differential Revision: D14070735

Pulled By: ezyang

fbshipit-source-id: ace2a672998b4e4a8094f52cbda5aa1cea6e3b42
 2019-02-16 00:01:33 -08:00
Xiang Gao
4fcab92d6c Move outplace ops to ATen (#16788) 
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
 2019-02-15 15:58:10 -08:00
Stefan Krah
a5e7b1d032 Use IndexError instead of RuntimeError in ATen CPU kernels 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/17049

Reviewed By: ezyang

Differential Revision: D14064700

Pulled By: fmassa

fbshipit-source-id: 3575db103bba5a7d82f574cbb082beca419151ec
 2019-02-13 10:19:28 -08:00
vishwakftw
0d95028bee Dispatch the correct legacy function for geqrf_out and ormqr_out (#16964) 
Summary:
This fixes the segfault.

Changelog:
- Modify the function calls in LegacyDefinitions for `geqrf_out` and `ormqr_out`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16964

Differential Revision: D14025985

Pulled By: gchanan

fbshipit-source-id: aa50e2c1694cbf3642273ee14b09ba12625c7d33
 2019-02-12 13:48:51 -08:00
Ivan Ogasawara
8b4dea3f56 Added scientific notation on set_printoptions (#16876) 
Summary:
This PR fixes #15683
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16876

Differential Revision: D14021703

Pulled By: soumith

fbshipit-source-id: 1f603a7d24e331831d8d389f4a704c6a5b070b0c
 2019-02-11 04:55:12 -08:00
Hameer Abbasi
73d7ecd183 Add abs for ByteTensor and CharTensor. (#16893) 
Summary:
Fixes #15089
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16893

Differential Revision: D14020115

Pulled By: ezyang

fbshipit-source-id: 6f3be6ed28d2d37667159be45959d400bc473451
 2019-02-10 19:31:57 -08:00
Johannes M Dieterich
23e1c55cc0 enable unit tests working on ROCm 2.1 (#16871) 
Summary:
This is the first round of enabling unit tests that work on ROCm 2.1 in my tests.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16871

Differential Revision: D13997662

Pulled By: bddppq

fbshipit-source-id: d909a3f7dd5fc8f85f126bf0613751c8e4ef949f
 2019-02-09 00:30:50 -08:00
Sebastian Messmer
6750e1e3e9 C10_REGISTER_CAFFE2_OPERATOR: Macro for registering c2 kernels (#16548) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16548

With this macro, a caffe2 operator can now directly be registered with c10.
No need to write custom wrapper kernels anymore.

Differential Revision: D13877076

fbshipit-source-id: e56846238c5bb4b1989b79855fd44d5ecf089c9c
 2019-02-07 13:58:14 -08:00
Brennan Vincent
1ce188c510 logsumexp for multiple dimensions (#16475) 
Summary:
Move `logsumexp` and `max_values` to `TensorIterator` and use it to make `logsumexp` work for multiple dimensions.

Timings on a tensor of shape `(10,1000000,10)`, for each combination of (cpu, single-threaded cpu, gpu) and dimension:

**before**
208 ms ± 2.72 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
279 ms ± 5.07 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
199 ms ± 2.64 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
1.11 s ± 33.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
1.25 s ± 25.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
1.11 s ± 6.83 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
15.4 ms ± 1.02 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
132 ms ± 30.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
39.6 ms ± 19.1 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

**after**
199 ms ± 8.23 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
307 ms ± 8.73 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
207 ms ± 7.62 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
1.16 s ± 8.92 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
1.26 s ± 47.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
1.13 s ± 13.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
15.4 ms ± 868 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)
132 ms ± 27.6 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
39.6 ms ± 21.8 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16475

Differential Revision: D13855746

Pulled By: umanwizard

fbshipit-source-id: aaacc0b967c3f89073487e1952ae6f76b7bd7ad3
 2019-02-05 08:32:11 -08:00
Edward Yang
6c04224cd8 Revert "Move outplace ops to ATen (#12413)" (#16731) 
Summary:
This reverts commit f660d3ae19.

cc zasdfgbnm

Reasoning at https://github.com/pytorch/pytorch/pull/12413#issuecomment-460424129
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16731

Differential Revision: D13948022

Pulled By: ezyang

fbshipit-source-id: b10669cf03679e306850314b7b5b08bed0839e19
 2019-02-04 19:30:04 -08:00
vishwakftw
6d86bc7c3f Fix issue with scalars and __rpow__ (#16687) 
Summary:
Changelog:

- Modify __rpow__ function in tensor.py to adapt to scalars
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16687

Differential Revision: D13936720

Pulled By: soumith

fbshipit-source-id: b0c8727968b04efbc6e7461807c812d962f03370
 2019-02-02 18:55:51 -08:00
Xiang Gao
f660d3ae19 Move outplace ops to ATen (#12413) 
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
 2019-01-31 16:09:45 -08:00
Jacie Fan
a7796bc24d CUDA histogram implementation 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15842

Reviewed By: zou3519

Differential Revision: D13868982

Pulled By: jaciefan

fbshipit-source-id: bce81dc121c4538d204047506f8f14d0b4d8f905
 2019-01-30 11:36:20 -08:00
Sebastian Messmer
7c66ad7455 Add test case for calling c10 ops from pytorch 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16062

Reviewed By: ezyang

Differential Revision: D13628955

fbshipit-source-id: f6ed3f07db2675bd9ae9251da990ca7b8c963717
 2019-01-29 18:22:52 -08:00
SsnL
ded6fb0293 Add stack & cat support for CPU Half (#16389) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/6968

Needed for #14705
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16389

Differential Revision: D13861446

Pulled By: gchanan

fbshipit-source-id: 7b8700b95aaf252d9669693dbddccb2302e58409
 2019-01-29 13:06:29 -08:00
Junjie Bai
17d7818578 Fix lint errors introduced in pytorch/pytorch@ceece5d (#16454) 
Summary:
ifedan

```
./test/common_utils.py:748:1: E302 expected 2 blank lines, found 1
./test/test_torch.py:1235:5: E303 too many blank lines (2)
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16454

Differential Revision: D13844905

Pulled By: bddppq

fbshipit-source-id: 3dc7c740d86310a8efc9864d7c7798fda8257a21
 2019-01-28 11:29:11 -08:00
Igor Fedan
ceece5dd0f CPU implementation of torch.cdist (#16168) 
Summary:
cdist is used for calculating distances between collections of observations.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16168

Differential Revision: D13739147

Pulled By: ifedan

fbshipit-source-id: 9419c2c166891ac7db40672c72f17848f0b446f9
 2019-01-28 09:16:32 -08:00
Xiang Gao
c5e1b469be Return namedtuples from torch.* function with multiple return arguments for C++ operators (#15429) 
Summary:
Partially fixes: https://github.com/pytorch/pytorch/issues/394

Implementation detail:

Codegen is modified to generate codes that looks like below:
```C++
static PyObject * THPVariable_svd(PyObject* self_, PyObject* args, PyObject* kwargs)
{
  HANDLE_TH_ERRORS
  static PythonArgParser parser({
    "svd(Tensor input, bool some=True, bool compute_uv=True, *, TensorList[3] out=None)",
  }, /*traceable=*/true);

  ParsedArgs<6> parsed_args;
  auto r = parser.parse(args, kwargs, parsed_args);
  static PyStructSequence_Field fields0[] = {
    {"U", ""}, {"S", ""}, {"V", ""}, {nullptr}
  };
  static PyStructSequence_Desc desc0 = {
    "torch.return_types.svd_out", nullptr,
    fields0, 3
  };
  static PyTypeObject type0;
  static bool namedtuple_type_initialized0 = false;
  if (!namedtuple_type_initialized0) {
    PyStructSequence_InitType(&type0, &desc0);
    namedtuple_type_initialized0 = true;
  }
  static PyStructSequence_Field fields1[] = {
    {"U", ""}, {"S", ""}, {"V", ""}, {nullptr}
  };
  static PyStructSequence_Desc desc1 = {
    "torch.return_types.svd", nullptr,
    fields1, 3
  };
  static PyTypeObject type1;
  static bool namedtuple_type_initialized1 = false;
  if (!namedtuple_type_initialized1) {
    PyStructSequence_InitType(&type1, &desc1);
    namedtuple_type_initialized1 = true;
  }
  if (r.idx == 0) {
    if (r.isNone(3)) {
      return wrap(&type1, dispatch_svd(r.tensor(0), r.toBool(1), r.toBool(2)));
    } else {
      auto results = r.tensorlist_n<3>(3);
      return wrap(&type0, dispatch_svd(r.tensor(0), r.toBool(1), r.toBool(2), results[0], results[1], results[2]));
    }
  }
  Py_RETURN_NONE;
  END_HANDLE_TH_ERRORS
}
```
Types are defined as static member of `THPVariable_${op_name}` functions, and initialized at the first time the function is called.

When parsing function prototypes in `native_functions.yaml`, the parser will set the specified name as `field_name` when see things like `-> (Tensor t1, ...)`. These field names will be the field names of namedtuple. The class of namedtuples will be named `torch.return_types.${op_name}`.

In some python 2, `PyStructSequence` is not a subtype of tuple, so we have to create some functions to check if an object is a tuple or namedtuple for compatibility issue.

Operators in `native_functions.yaml` are changed such that only `max` and `svd` are generated as namedtuple. Tests are added for these two operators to see if the return value works as expected. Docs for these two ops are also updated to explicitly mention the return value is a namedtuple. More ops will be added in later PRs.

There is some issue with Windows build of linker unable to resolve `PyStructSequence_UnnamedField`, and some workaround is added to deal with this case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15429

Differential Revision: D13709678

Pulled By: ezyang

fbshipit-source-id: 23a511c9436977098afc49374e9a748b6e30bccf
 2019-01-22 11:12:18 -08:00
Shen Li
1ff864712b Port legacy any(*) to ATen 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/15547

Differential Revision: D13549495

Pulled By: mrshenli

fbshipit-source-id: 09a065a8ffa7d73f409759b779c7314cc87f4853
 2019-01-18 10:32:19 -08:00
Gregory Chanan
595f767880 Revert batched pdist, improve existing kernel, add test (#15901) 
Summary:
1) Reverts https://github.com/pytorch/pytorch/pull/12302 which added support for batched pdist. Except I kept the (non-batched) test improvements that came with that PR, because they are nice to have.  Motivation: https://github.com/pytorch/pytorch/issues/15511
2) For the non-batched pdist, improved the existing kernel by forcing fp64 math and properly checking cuda launch errors
3) Added a 'large tensor' test that at least on my machine, fails on the batch pdist implementation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15901

Reviewed By: ezyang

Differential Revision: D13616730

Pulled By: gchanan

fbshipit-source-id: 620d3f9b9acd492dc131bad9d2ff618d69fc2954
 2019-01-17 10:44:43 -08:00
Shen Li
a2af554e6f Port legacy all(*) to ATen (#15540) 
Summary:
Questions:

1. ~This PR disables `common_dtype` computation [in `TensorIterator.cpp`](https://github.com/mrshenli/pytorch/blob/all/aten/src/ATen/native/TensorIterator.cpp#L489-L491) for `all*` operators. The reason is that, [this code](https://github.com/mrshenli/pytorch/blob/all/aten/src/ATen/native/TensorIterator.cpp#L120) otherwise complains type mismatch, where the `op.tensor` is `type Variable[CPUByteType]` while the `op` is `CPUByteType`. I am not sure if this is the right solution for this problem.~

2. Should I clean up all occurrences of `_th_all` and `_th_all_out` (and `logicalAnd`, `logicalAndAll`)?

3. Do I need to implement derivatives for `all`?

gchanan

Benchmark:

<img width="590" alt="screen shot 2018-12-26 at 3 24 31 pm" src="https://user-images.githubusercontent.com/16999635/50456505-e9596a00-0922-11e9-844e-00c4b4aad7ca.png">

<img width="587" alt="screen shot 2018-12-26 at 3 26 10 pm" src="https://user-images.githubusercontent.com/16999635/50456509-ef4f4b00-0922-11e9-96bf-0a30c8574fe7.png">

<img width="590" alt="screen shot 2018-12-26 at 3 26 54 pm" src="https://user-images.githubusercontent.com/16999635/50456510-ef4f4b00-0922-11e9-8a63-e47988843cc8.png">

<img width="589" alt="screen shot 2018-12-26 at 3 27 16 pm" src="https://user-images.githubusercontent.com/16999635/50456511-ef4f4b00-0922-11e9-9004-2518aebcdc6e.png">
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15540

Differential Revision: D13548938

Pulled By: mrshenli

fbshipit-source-id: 5a2e5eef1047decb4c79906cb9f3332034908c9c
 2019-01-16 09:06:26 -08:00
Xiang Gao
1065e7cd24 Add itertools.{prod, combinations, combinations_with_replacement} like op to pytorch (#9393) 
Summary:
closes https://github.com/pytorch/pytorch/issues/7580
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9393

Differential Revision: D13659628

Pulled By: zou3519

fbshipit-source-id: 3a233befa785709395a793ba8833413be394a6fd
 2019-01-15 08:31:22 -08:00
Brennan Vincent
bc233fe405 var for multiple dimensions (#15892) 
Summary:
Timings are the same as for `std` .
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15892

Differential Revision: D13651173

Pulled By: umanwizard

fbshipit-source-id: a26bf1021dd972aa9e3e60fb901cd4983bfa190f
 2019-01-14 20:17:42 -08:00
Christian Puhrsch
d33159a426 Undo norm optimizations and add more documentation for parallel.h (#15885) 
Summary:
See https://github.com/pytorch/pytorch/issues/15602
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15885

Differential Revision: D13614841

Pulled By: cpuhrsch

fbshipit-source-id: 5d3e45f499d36ac287dbbc2e45798aa51eb5bfdf
 2019-01-11 13:32:35 -08:00
Brennan Vincent
70dd44f6a8 Match NumPy by considering NaNs to be larger than any number when sorting (#15886) 
Summary:
Fixes #15764
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15886

Differential Revision: D13612971

Pulled By: umanwizard

fbshipit-source-id: 91f552a25d1fd108f2f0b10e09a0ce0364f8c21e
 2019-01-11 08:14:11 -08:00
Gregory Chanan
b7cdeb3fc3 Port empty_strided to ATen. (#15948) 
Summary:
Turns out this has basically been implemented already in Resize.h / Resize.cuh.
Also added some testing, basically just to check that empty_strided behaves equivalently to as_strided.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15948

Differential Revision: D13631098

Pulled By: gchanan

fbshipit-source-id: eb0e04eead45e4cff393ebde340f9d265779e185
 2019-01-11 07:58:05 -08:00
vishwakftw
b4c3268b23 Batched upper triangular, lower triangular (#15257) 
Summary:
Changelog:

- Implements `triu` and `tril` for batches of 2D tensors.
- Remove TH/THC binding for `tril`
- Fix CUDA implementation
- Update docstrings for tril and triu.
- Remove mask-based `triu` and `tril` in cholesky forward and backward.
- Remove batched tril in torch.distributions.utils
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15257

Differential Revision: D13613888

Pulled By: mrshenli

fbshipit-source-id: 0949a05b9b8e974c1acfaf02a6284848ec5cc1c4
 2019-01-09 19:46:39 -08:00
zou3519
f0c2a9a7b6 Add torch.bincount() test case on sliced tensor (#15835) 
Summary:
This was causing a problem in #15735 but appears to have been fixed.
Adding this test to prevent regressions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15835

Differential Revision: D13600282

Pulled By: zou3519

fbshipit-source-id: d9939e74d372be71c50122a5f6a615fbd7fa4df6
 2019-01-09 07:31:19 -08:00
vishwakftw
95febdfacc Add is_floating_point to docs (#15704) 
Summary:
Fixes #15700 .

Changelog:

- Expose torch.*.is_floating_point to docs

Differential Revision: D13580734

Pulled By: zou3519

fbshipit-source-id: 76edb4af666c08237091a2cebf53d9ba5e6c8909
 2019-01-07 10:43:22 -08:00
mruberry
b6a8c45f57 Removes print statements from test_torch.py (#15747) 
Summary:
These print statements do not affect the test, and tests (generally) shouldn't print.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15747

Differential Revision: D13587289

Pulled By: soumith

fbshipit-source-id: c758793c9e35faf02bacba6c7c6d072f7c40453f
 2019-01-05 09:07:27 -08:00
Shen Li
efc3d6b65d Fix vec256 inversion (#15659) 
Summary:
soumith zou3519

I was browsing the code, and think `vec256_int.h` might need a minor revision, but not 100% sure.

1. It currently invert the result by `XOR` with 0. Should it `XOR` with 1 instead?
~2. AVX2 logical operations would set all bits in a byte/word/... to `1` if the condition holds. So functions, such as `_mm256_cmpeq_epi64 ` would return `0/-1` instead of `0/1`. Should it be masked with `1` to make sure it returns 0/1?~

~Would I be correct if I assume that the code revised below is not yet activated, but will be after we port legacy code to ATen?~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15659

Differential Revision: D13565929

Pulled By: mrshenli

fbshipit-source-id: 8ae3daf256c3d915dd855a2215c95275e899ea8c
 2019-01-02 21:32:44 -08:00
surgan12
b52420742d clamp fixes (#15479) 
Summary: fix to #15338 .

Differential Revision: D13564343

Pulled By: soumith

fbshipit-source-id: be64b572945533e10ae6f627d335b47f093720a3
 2019-01-01 23:12:17 -08:00
vishwakftw
7bb41e3953 Make btriunpack work for high dimensional batches and faster than before (#15286) 
Summary:
Changelog:
- Optimize btriunpack by using `torch.where` instead of indexing, inplace operations instead of out place operations and avoiding costly permutations by computing the final permutation over a list.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15286

Differential Revision: D13562038

Pulled By: soumith

fbshipit-source-id: e2c94cfab5322bf1d24bf56d7b056619f553acc6
 2018-12-30 12:42:07 -08:00
Vishwak Srinivasan
9c8d8eab9d Remove TH/THC link for gesv (#15510) 
Summary:
This PR removes the TH/THC binding for gesv.

Changelog:
- Remove TH/THC binding
- Port single matrix case to ATen
- Enable test_gesv for CUDA as well
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15510

Differential Revision: D13559990

Pulled By: soumith

fbshipit-source-id: 9da2825e94d3103627e719709e6b1f8b521a07fb
 2018-12-28 16:54:27 -08:00
Will Feng
7b87ecae37 Move autograd metadata from VariableImpl to TensorImpl (#13827) 
Summary:
Changes originally in this PR:
1. Move Variable::Impl data members into TensorImpl as `AutogradMeta` struct
2. Change Variable::Impl functions to use data members in `AutogradMeta` struct
3. Add `shallow_copy_and_detach()` function to each subclass of TensorImpl
4. Do shallow copy when the user calls `make_variable(tensor)` / `make_variable_view(tensor)` / `variable.set_data(tensor)` / `variable.detach()`

Changes moved from https://github.com/pytorch/pytorch/pull/13645:
1. Add a flag to Variable to disallow size/stride/storage_ptr changes from in-place operations such as `resize_` / `resize_as_` / `set_` / `transpose_`, and set this flag to true when people call `tensor.data` in Python.
2. Write text in the docs to actively discourage changing the shape or storage of `tensor_detached` and expecting `tensor` to also be updated.

This is the 1st+2nd PR mentioned in https://github.com/pytorch/pytorch/issues/13638.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13827

Differential Revision: D13507173

Pulled By: yf225

fbshipit-source-id: b177b08438d534a8197e34e1ad4a837e2db0ed6a
 2018-12-26 16:34:24 -08:00
Frank Zhang
d4712ee218 Added correct isinf handling for Integral tensors (#15489) 
Summary:
Currently torch.isinf on integral tensor will raise RuntimeError: value cannot be converted to type int16_t without overflow: inf.
This pr will suppress the error and return false(0) for all integral tensors. The behavior will also be consistent with np.isinf
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15489

Reviewed By: zou3519

Differential Revision: D13540786

Pulled By: flashhack

fbshipit-source-id: e730dea849da6a59f3752d347bcfbadfd12c6483
 2018-12-26 06:36:09 -08:00
SsnL
521894c490 Allow converting char tensor to numpy; add [fi]info.min (#15046) 
Summary:
https://github.com/pytorch/pytorch/pull/14710 with test fixed.

Also added `finfo.min` and `iinfo.min` to get castable tensors.

cc soumith
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15046

Reviewed By: soumith

Differential Revision: D13429388

Pulled By: SsnL

fbshipit-source-id: 9a08004419c83bc5ef51d03b6df3961a9f5dbf47
 2018-12-24 09:11:24 -08:00
Gao, Xiang
a47749cb28 Add at::one_hot (#15208) 
Summary: Closes: https://github.com/pytorch/pytorch/issues/15060

Differential Revision: D13528014

Pulled By: ezyang

fbshipit-source-id: 5a18689a4c5638d92f9390c91517f741e5396293
 2018-12-20 14:24:58 -08:00
Shen Li
06a7cb5901 Implementing cuda kernel for tril_indices and triu_indices (#15203) 
Summary:
Followup PR of #14904, and the stretch goal of #12653.

Directly calculate coordinates in the original tensor using column index in the result tensor. Every GPU thread takes care of a column (two numbers) in the output tensor.

The implementation detects and handles precision loss during calculating the square root of a `int64_t` variable, and supports tensors with up to `row * column = 2 ^ 59` numbers.

Algorithm details are describe in [comments of TensorFactories.cu](23ddb6f58a/aten/src/ATen/native/cuda/TensorFactories.cu (L109-L255)).

zou3519
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15203

Reviewed By: zou3519

Differential Revision: D13517695

Pulled By: mrshenli

fbshipit-source-id: 86b305d22cac08c8962a3b0cf8e9e620b7ec33ea
 2018-12-20 10:23:38 -08:00