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Author SHA1 Message Date
Yuanyuan Chen
c79dfdc655 Enable all PIE rules on ruff (#165814) 
This PR enables all PIE rules on ruff, there are already some enabled rules from this family, the new added rules are
```
PIE796  Enum contains duplicate value: {value}
PIE808  Unnecessary start argument in range
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165814
Approved by: https://github.com/ezyang
 2025-10-18 06:40:12 +00:00
can-gaa-hou
0256f91558 [BUG] MaxUnpool2d/3d should check output dim before accessing its elements (#163507) 
Fixes #163409
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163507
Approved by: https://github.com/malfet, https://github.com/Skylion007
 2025-09-22 21:36:48 +00:00
can-gaa-hou
ce5637be29 Fix invalid indices bug for max_unpool2d/3d on MPS (#163036) 
Fixes #163035
Pull Request resolved: https://github.com/pytorch/pytorch/pull/163036
Approved by: https://github.com/kulinseth, https://github.com/malfet

Co-authored-by: Nikita Shulga <2453524+malfet@users.noreply.github.com>
 2025-09-19 05:13:21 +00:00
can-gaa-hou
29ea6254a0 [Bug] Add more boundary check for FractionalMaxPool3d (#161876) 
This PR aims to fix the bug mentioned at [#161853](https://github.com/pytorch/pytorch/issues/161853#issuecomment-3240695121)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/161876
Approved by: https://github.com/malfet
 2025-09-16 06:59:02 +00:00
PyTorch MergeBot
468c1f9e9d Revert "[nn] Assert parsed iterable arguments are an appropriate length (#162340)" 
This reverts commit b5e6e58050.

Reverted https://github.com/pytorch/pytorch/pull/162340 on behalf of https://github.com/huydhn due to Sorry for reverting your change but it seems to break an MPS tests on ExecuTorch ([comment](https://github.com/pytorch/pytorch/pull/162340#issuecomment-3282676242))
 2025-09-11 21:22:57 +00:00
Benjamin Glass
b5e6e58050 [nn] Assert parsed iterable arguments are an appropriate length (#162340) 
Fixes #162327
Pull Request resolved: https://github.com/pytorch/pytorch/pull/162340
Approved by: https://github.com/Skylion007
 2025-09-10 15:15:49 +00:00
Nikita Shulga
e06b110f73 [Testing] Add MPS to NATIVE_DEVICES (#153835) 
This would allow me to enable more opinfo tests against MPS device eventually and supposed to be a very simple test, but actually required minor adjustments to lots of test files, namely:
- Introduce `all_mps_types_and` that is very similar to `all_types_and`, but skips `float64`
- Decorate lots of tests with `@dtypesIfMPS(*all_mps_types())`
- Skip `test_from_dlpack_noncontinguous` as it currently crashes (need to be fixed)
- Add lots of `expectedFailureIfMPS`
- Delete all `@onlyNativeDeviceTypesAnd("mps")`

&lt;sarcasm&gt; I love how well documented this variable are &lt;/sarcasm&gt;

Pull Request resolved: https://github.com/pytorch/pytorch/pull/153835
Approved by: https://github.com/Skylion007
 2025-08-05 18:57:35 +00:00
Kurt Mohler
52b9af163c Add avg_pool3d for MPS (#158877) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/158877
Approved by: https://github.com/malfet
 2025-07-29 15:22:22 +00:00
zeshengzong
2b19d85d70 FractionalMaxPool3d add kernel_size check (#155549) 
Fixes #96316

## Test Result

```python
>>> import torch
>>> from torch.func import jacrev, grad, vmap
>>>
>>> torch.manual_seed(420)
<torch._C.Generator object at 0x7fe4767810d0>
>>>
>>> input = torch.randn(1, 1, 5, 5, 5, requires_grad=True)
>>>
>>> def func(input):
...     model = torch.nn.FractionalMaxPool3d(kernel_size=0, output_size=(1, 1, 1))
...     output = model(input)
...     return output
...
>>>
>>> func(input).sum().backward()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 2, in func
  File "/home/zong/code/pytorch/torch/nn/modules/pooling.py", line 1054, in __init__
    raise ValueError(f"kernel_size must greater than 0, but got {kernel_size}")
ValueError: kernel_size must greater than 0, but got 0

```

![image](https://github.com/user-attachments/assets/52780ce7-3951-4d1c-95a4-5ce2bf65c727)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/155549
Approved by: https://github.com/albanD
 2025-07-10 04:55:06 +00:00
Kurt Mohler
510c398a4f Add max_pool3d backward pass for MPS (#157498) 
Note on backward precision over fp16:

A float16 number has 10 bits of mantissa, 5 bits of exponent, and 1 bit for the sign. If the sign bit is positive, then with a mantissa $m$ and exponent $e$ represented in base 10, the number that the float16 format represents is $(1 + m / 1024)  \exp2(e)$. ([source](https://en.wikipedia.org/wiki/Half-precision_floating-point_format))

Consider adding two numbers $a$ and $b$ which have arbitrary mantissas, and say their exponents are $e_a = 1$ (so $2 \le a \lt 4$) and $e_b=-3$ (so $0.175 \le b \lt 0.25$). Assume that the result has the same exponent as $a$. Since the exponents differ by 4, we'll effectively need to truncate the 4 rightmost bits of $b$'s mantissa, which would introduce a maximum error on the order of $(2^4 / 1024)  \exp2(-3) \approx 0.002$.

The error is nearly the same if $e_b = -2$ (so $0.25 \le b \lt 0.5$), where the 3 rightmost bits are truncated, giving a maximum error on the order of $(2^3 / 1024)  \exp2(-2) \approx 0.002$. Same for $e_b=-1$.

So if we're adding up nine different numbers that all have exponents -3, -2, or -1, and they sum to a number with exponent 1, then we would expect a maximum error of several times greater than 0.002. In my comments above, summing those particular nine numbers in different ways gave results that ranged between 3.1816 and 3.1758, a difference of $0.0058 \approx 2.9  * 0.002$.

That's within the acceptable bounds, and we can safely just increase the error tolerance used in test_output_grad_match for the case of max_pool3d_backward with float16.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/157498
Approved by: https://github.com/malfet
 2025-07-07 19:46:44 +00:00
Sun, Jiayi
496bbf38be add grad_output shape check for adaptive_avg_pool2d_backward (#145241) 
Fix https://github.com/pytorch/pytorch/issues/145070.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/145241
Approved by: https://github.com/malfet, https://github.com/eqy
 2025-03-20 14:10:31 +00:00
cyy
df458be4e5 [4/N] Apply py39 ruff and pyupgrade fixes (#143257) 
```torch/fx/passes/annotate_getitem_nodes.py``` was changed to support the new type hinting annotations.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/143257
Approved by: https://github.com/justinchuby, https://github.com/albanD
 2025-01-04 10:47:51 +00:00
Sun, Jiayi
d2b83aa122 add grad_output shape check for fractional_max_pool2d_backward (#141666) 
Fix https://github.com/pytorch/pytorch/issues/141102.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141666
Approved by: https://github.com/mingfeima, https://github.com/malfet
 2024-12-19 22:47:02 +00:00
Sun, Jiayi
fa1a4a91e9 add batch_size check for max_pool2d_backward (#141657) 
Fix https://github.com/pytorch/pytorch/issues/140923.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141657
Approved by: https://github.com/mingfeima, https://github.com/malfet
 2024-12-19 06:01:41 +00:00
Sun, Jiayi
b588a78ca3 add grad_output shape check for adaptive_max_pool2d_backward and adaptive_max_pool3d_backward (#141663) 
Fix https://github.com/pytorch/pytorch/issues/141099, https://github.com/pytorch/pytorch/issues/141100.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/141663
Approved by: https://github.com/mingfeima, https://github.com/malfet
 2024-12-18 17:44:27 +00:00
Tom Ritchford
c947a7d38e Fix unused Python variables in test/nn (#143396) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143396
Approved by: https://github.com/mikaylagawarecki
 2024-12-18 03:30:54 +00:00
zeshengzong
cb71bcc542 Replace clone.detach with detach.clone (#140264) 
Fixes #64532

As state in issue, replace `clone.detach` by `detach.clone`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/140264
Approved by: https://github.com/soulitzer
 2024-11-13 07:01:02 +00:00
Yuanhao Ji
279ddfc6ee Add type check for dilation in torch.quantized_max_pool3d() (#137845) 
Fixes #136716

repro:

```python
import torch

input = torch.randn([1, 1, 1, 1, 1])
input = torch.quantize_per_tensor(input, 0.1, 10, torch.qint32)
torch.quantized_max_pool3d(input, (1, 1, 1), (1, 1, 1), (0, 0, 0), (-3, 1, 1)) # crash

input = torch.randn([1, 1, 1, 1, 1])
input = torch.quantize_per_tensor(input, 0.1, 10, torch.qint32)
result = torch.nn.functional.max_pool3d(input, (1, 1, 1), (1, 1, 1), (0, 0, 0), (-3, 1, 1))  # crash
```

result:

```
RuntimeError: Expected dilation >= 1
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/137845
Approved by: https://github.com/albanD
 2024-10-21 16:15:57 +00:00
Siddharth Kotapati
e27c0048db Enable additional tests for MPS CI runs (#134356) 
As part of the follow up for https://github.com/pytorch/pytorch/issues/133520, adapting existing unused tests for use in MPS CI runs. Focusing on nhwc & other memory formatting tests

Pull Request resolved: https://github.com/pytorch/pytorch/pull/134356
Approved by: https://github.com/malfet, https://github.com/eqy, https://github.com/huydhn
 2024-10-04 21:52:38 +00:00
Xuehai Pan
fbe6f42dcf [BE][Easy][8/19] enforce style for empty lines in import segments in test/[k-p]*/ (#129759) 
See https://github.com/pytorch/pytorch/pull/129751#issue-2380881501. Most changes are auto-generated by linter.

You can review these PRs via:

```bash
git diff --ignore-all-space --ignore-blank-lines HEAD~1
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/129759
Approved by: https://github.com/justinchuby, https://github.com/ezyang
 2024-07-31 02:09:20 +00:00
Isuru Fernando
5f912f480c Fix max_pool2d decomposition for empty list and integer limits (#129106) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/129106
Approved by: https://github.com/peterbell10, https://github.com/lezcano, https://github.com/malfet
ghstack dependencies: #129096, #129097
 2024-06-24 22:19:42 +00:00
Yuanhao Ji
a625705290 Enable UFMT on all of test/nn (#123809) 
Part of: #123062

Ran lintrunner on:

- `test/nn`

with command:

```bash
lintrunner -a --take UFMT --all-files
```

Co-authored-by: Edward Z. Yang <ezyang@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123809
Approved by: https://github.com/mikaylagawarecki
 2024-04-12 18:32:25 +00:00
Mikayla Gawarecki
b5e83b8c50 Fix edge case for size 1 channels dim in AdaptiveMaxPool (#116482) 
Fixes https://github.com/pytorch/pytorch/issues/107842

Unlike `AdaptiveAvgPool`, `AdaptiveMaxPool` does not have a CUDA kernel for ChannelsLast. We workaround this by calling `contiguous()` on the input. However, there is an edge case when the channels dimension has size 1.

```python
>>> t = torch.randn(2, 1, 3, 3)
>>> t.stride()
(9, 9, 3, 1)
>>> t_c =  t.to(memory_format=torch.channels_last)
>>> t_c.stride()
(9, 1, 3, 1)  # (CHW, 1, CW, C)
>>> t_c.is_contiguous()
True  # contiguity check doesn't check strides for singleton dimensions
```

Since the CUDA kernel treats the batch,`B`, and  channels,`C`, dimensions as implicitly flattened and increments the data pointer for `input` to the start of the next plane using

669b182d33/aten/src/ATen/native/cuda/AdaptiveMaxPooling2d.cu (L67)

If our input falls into the aforementioned edge case, the `data_ptr` will not be incremented correctly. The simple fix for this is to calculate the stride for the channels dimension using $\prod_{i > 1}size(i)$

Analogous fix for the 3D case.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/116482
Approved by: https://github.com/albanD
 2023-12-28 15:02:29 +00:00
Tobias Ringwald
362bc6d7cb Fixed a segfault issue when passing an empty kernel to quantized_max_… (#116342) 
…pool1d.

Fixes #116323.

Reused the same check as for `max_pool1d`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/116342
Approved by: https://github.com/jerryzh168
 2023-12-27 01:22:49 +00:00
Aaron Gokaslan
6de28e92d2 [BE]: Apply FURB118 (prev): replaces unnecessary lambdas with operator. (#116027) 
This replaces a bunch of unnecessary lambdas with the operator package. This is semantically equivalent, but the operator package is faster, and arguably more readable. When the FURB rules are taken out of preview, I will enable it as a ruff check.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/116027
Approved by: https://github.com/malfet
 2023-12-20 19:35:08 +00:00
PyTorch MergeBot
a7bfa04da6 Revert "More markDynamoStrictTest (#115870)" 
This reverts commit 7f686c8fe1.

Reverted https://github.com/pytorch/pytorch/pull/115870 on behalf of https://github.com/jeanschmidt due to Breaking internal tests and builds, please check diff ([comment](https://github.com/pytorch/pytorch/pull/115870#issuecomment-1862997125))
 2023-12-19 15:40:57 +00:00
rzou
7f686c8fe1 More markDynamoStrictTest (#115870) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/115870
Approved by: https://github.com/voznesenskym
ghstack dependencies: #115845, #115855, #115856, #115857, #115858
 2023-12-15 05:26:54 +00:00
mingfeima
a8acd6c410 Add Half support for AvgPool2d on CPU (#109578) 
Add Half support for AvgPool2d (both channels last and channels first) on CPU

Pull Request resolved: https://github.com/pytorch/pytorch/pull/109578
Approved by: https://github.com/mingfeima, https://github.com/albanD
 2023-12-12 12:59:47 +00:00
CaoE
7963aaac41 add Half support for AdaptiveAvgPool2d and AdaptiveMaxPool2d on CPU (#102079) 
### Testing

Single core:

AdaptiveMaxPool2d:
shape | fp32 forward / ms | fp16 forward / ms | bf16 forward / ms | fp32 backward / ms | fp16 backward / ms | bf16 backward / ms
-- | -- | -- | -- | -- | -- | --
input size: (2, 56, 264, 264), output size: (100, 100)  | 71.5826 | 78.7460 | 85.7195 | 7.3925 | 6.0618 | 6.2596
input size: (2, 56, 264, 264), output size: (50, 50)  | 28.122 | 30.8572 | 36.6366 | 6.2645 | 3.4781 | 3.6628
input size: (32, 32, 100, 100), output size: (50, 50)  | 109.2978 | 115.0330 | 121.9500 | 13.4329 | 10.2769 | 12.1975
input size: (16, 4, 300, 300), output size: (100, 100) | 34.1849 | 36.5876 | 40.9862 | 4.7719 | 4.3362 | 4.1417

28 cores:

AdaptiveMaxPool2d:

shape | fp32 forward / ms | fp16 forward / ms | bf16 forward / ms | fp32 backward / ms | fp16 backward / ms | bf16 backward / ms
-- | -- | -- | -- | -- | -- | --
input size: (2, 56, 264, 264), output size: (100, 100)  | 3.1809 | 3.5057 | 3.6728 | 0.6657 | 0.3138 | 0.2934
input size: (2, 56, 264, 264), output size: (50, 50)  | 1.2779 | 1.3869 | 1.5238 | 0.4223 | 0.1775 | 0.1825
input size: (32, 32, 100, 100), output size: (50, 50)  | 4.7942 | 4.9670 | 5.2330 | 1.7146 | 0.6477 | 0.7001
input size: (16, 4, 300, 300), output size: (100, 100) | 1.9522 | 2.0879 | 2.3155 | 0.4370 | 0.3175 | 0.2828

Pull Request resolved: https://github.com/pytorch/pytorch/pull/102079
Approved by: https://github.com/jgong5, https://github.com/malfet
 2023-11-20 03:01:00 +00:00
Edward Z. Yang
40c44c2307 Force specialization on INT_LIST (#111216) 
Follow up on https://github.com/pytorch/pytorch/pull/95479

Fixes https://github.com/pytorch/pytorch/issues/111198

Fixes https://github.com/pytorch/pytorch/issues/111197

Fixes https://github.com/pytorch/pytorch/issues/111188

Fixes https://github.com/pytorch/pytorch/issues/111201

Fixes https://github.com/pytorch/pytorch/issues/111202

I can also do this for some other types, will do this stacked on top.

Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/111216
Approved by: https://github.com/voznesenskym
 2023-10-19 12:55:18 +00:00
Jerry Zhang
f2a1b93549 Back out "[quant] Support integer implementations for adaptive_avg_pool2d (#104226)" (#110316) 
Summary:
Original commit changeset: acdb5b34e3aa

Original Phabricator Diff: D47321689

Test Plan: opinfo tests in CI

Differential Revision: D49789403

Pull Request resolved: https://github.com/pytorch/pytorch/pull/110316
Approved by: https://github.com/kimishpatel
 2023-10-03 16:59:23 +00:00
CaoE
42f94d7e9f add Half support for maxpool on CPU (#98819) 
### Testing
Single socket (28 cores):

shape | fp32 forward / ms | fp16 forward / ms | bf16 forward / ms | fp32 backward / ms | fp16 backward / ms | bf16 backward / ms
-- | -- | -- | -- | -- | -- | --
size: (1, 56, 264, 264), kernel: 3,   stride: 1, mem_format: contig | 4.12895 | 6.9669 | 5.30297 | 0.55775 | 1.98917 | 0.72233
size: (1, 56, 264, 264), kernel: 3,   stride: 1, mem_format: CL | 0.85093 | 1.88813 | 1.38063 | 5.5742 | 36.5086 | 10.58552
size: (32, 16, 200, 200), kernel: 3,   stride: 1, mem_format: contig | 22.37212 | 37.90383 | 30.94482 | 6.85868 | 10.6116 | 3.9993
size: (32, 16, 200, 200), kernel: 3,   stride: 1, mem_format: CL | 5.41658 | 4.71098 | 4.66578 | 6.69875 | 14.7171 | 5.1167
size: (32, 32, 100, 100), kernel: 3,   stride: 1, mem_format: contig | 10.69831 | 18.0468 | 13.71657 | 2.61192 | 4.96172 | 1.68635
size: (32, 32, 100, 100), kernel: 3,   stride: 1, mem_format: CL | 2.52637 | 2.0096 | 2.0055 | 2.60314 | 7.2093 | 2.49843
size: (4, 19, 10, 16, 16), kernel: 3,   stride: 1, mem_format: contig | 0.47605 | 0.88398 | 0.65326 | 0.06525 | 0.115489 | 0.0674
size: (4, 19, 10, 16, 16), kernel: 3,   stride: 1, mem_format: CL3d | 0.10902 | 0.25293 | 0.157475 | 0.11386 | 0.53319 | 0.17836

Single core:

shape | fp32 forward / ms | fp16 forward / ms | bf16 forward / ms | fp32 backward / ms | fp16 backward / ms | bf16 backward / ms
-- | -- | -- | -- | -- | -- | --
size: (1, 56, 264, 264), kernel: 3,   stride: 1, mem_format: contig | 90.9809 | 163.473 | 126.1276 | 6.57721 | 41.40833 | 11.82505
size: (1, 56, 264, 264), kernel: 3,   stride: 1, mem_format: CL | 9.88405 | 38.39137 | 29.62069 | 7.10636 | 36.97535 | 11.0525
size: (32, 16, 200, 200), kernel: 3,   stride: 1, mem_format: contig | 476.782 | 855.4769 | 648.2248 | 46.6488 | 219.2586 | 67.10599
size: (32, 16, 200, 200), kernel: 3,   stride: 1, mem_format: CL | 80.29271 | 91.33854 | 87.80345 | 48.81692 | 203.9974 | 63.39004
size: (32, 32, 100, 100), kernel: 3,   stride: 1, mem_format: contig | 235.2113 | 419.0799 | 315.4284 | 20.6049 | 107.1524 | 32.39169
size: (32, 32, 100, 100), kernel: 3,   stride: 1, mem_format: CL | 29.47653 | 33.54905 | 32.82823 | 22.59674 | 98.5586 | 30.05763
size: (4, 19, 10, 16, 16), kernel: 3,   stride: 1, mem_format: contig | 7.90684 | 13.9208 | 10.03272 | 0.23725 | 1.35269 | 0.41728
size: (4, 19, 10, 16, 16), kernel: 3,   stride: 1, mem_format: CL3d | 2.33638 | 3.36894 | 2.64635 | 0.26535 | 1.244 | 0.38895

Pull Request resolved: https://github.com/pytorch/pytorch/pull/98819
Approved by: https://github.com/mingfeima, https://github.com/mikaylagawarecki
 2023-09-05 18:23:41 +00:00
CaoE
3267996372 add channel last 3d support for maxpool3d on CPU (#97775) 
### Testing
Single socket (28 cores):

shape | fp32 forward / ms | bf16 forward / ms | fp32 backward / ms  | bf16 backward / ms
-- | -- | -- | -- | --
size: (1, 56, 264, 264), kernel: 3,   stride: 1, mem_format: contig | 3.959584 | 5.493402 | 0.557232 | 0.568485
size: (1, 56, 264, 264), kernel: 3,   stride: 1, mem_format: CL | 0.815511 | 1.351261 | 5.710506 | 10.57506
size: (32, 32, 100, 100), kernel: 3,   stride: 1, mem_format: contig  | 10.63426 | 15.28637 | 2.67656 | 1.71365
size: (32, 32, 100, 100), kernel: 3,   stride: 1, mem_format: CL | 2.63570 | 2.05532 | 2.55452 | 2.33923
size: (4, 19, 10, 16, 16), kernel:   3, stride: 1, mem_format: contig | 0.375469 | 0.479748 | 0.066364 | 0.065155
size: (4, 19, 10, 16, 16), kernel:   3, stride: 1, mem_format: CL3d | 0.112197 | 0.112326 | 0.111697 | 0.145364

Single core:

shape | fp32 forward / ms | bf16 forward / ms | fp32 backward / ms | bf16 backward / ms
-- | -- | -- | -- | --
size: (1, 56, 264, 264), kernel: 3,   stride: 1, mem_format: contig | 92.16582 | 128.6513 | 6.684325 | 12.21541
size: (1, 56, 264, 264), kernel: 3,   stride: 1, mem_format: CL | 10.14318 | 29.80297 | 7.350142 | 11.25323
size: (32, 32, 100, 100), kernel: 3,   stride: 1, mem_format: contig | 238.55453 | 331.89967 | 19.694657 | 32.78853
size: (32, 32, 100, 100), kernel: 3,   stride: 1, mem_format: CL | 30.17079 | 32.75628 | 22.44543 | 30.17796
size: (4, 19, 10, 16, 16), kernel:   3, stride: 1, mem_format: contig | 7.474389 | 9.937217 | 0.236015 | 0.434229
size: (4, 19, 10, 16, 16), kernel:   3, stride: 1, mem_format: CL3d | 2.318954 | 2.469444 | 0.262125 | 0.401361

Pull Request resolved: https://github.com/pytorch/pytorch/pull/97775
Approved by: https://github.com/jgong5, https://github.com/mikaylagawarecki
 2023-08-26 00:21:27 +00:00
Huy Do
d9460bb8f8 Update test_MaxUnpool_index_errors XFAIL after #107483 (#107658) 
After https://github.com/pytorch/pytorch/pull/107483 which reverted https://github.com/pytorch/pytorch/pull/95300, these tests are not XFAIL anymore.  So now we know the root cause of https://github.com/pytorch/pytorch/issues/103854.

As this is failing slow jobs in trunk atm, i.e. 6981bcbc35, I'm moving these tests back.

### Testing

Run locally and all tests passes.

```
PYTORCH_TEST_WITH_SLOW=1 PYTORCH_TEST_SKIP_FAST=1 python test/nn/test_pooling.py -k test_MaxUnpool_index_errors
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107658
Approved by: https://github.com/PaliC
 2023-08-22 22:36:35 +00:00
Justin Chu
de8bd108b4 [BE] Enable ruff's UP rules in pyproject.toml (#105437) 
Signed-off-by: Justin Chu <justinchu@microsoft.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/105437
Approved by: https://github.com/huydhn, https://github.com/malfet, https://github.com/Skylion007
 2023-07-21 19:14:52 +00:00
Justin Chu
79c5e33349 [BE] Enable ruff's UP rules and autoformat nn/ mps/ and torch/ (#105436) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105436
Approved by: https://github.com/malfet, https://github.com/albanD
 2023-07-21 07:38:46 +00:00
Jerry Zhang
1a661639f7 [quant] Support integer implementations for adaptive_avg_pool2d (#104226) 
Summary:
This is needed for representing quantized model in pt2 export quantization flow

Test Plan:
tested by opinfo, python test/test_ops.py

Summary:

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

Pull Request resolved: https://github.com/pytorch/pytorch/pull/104226
Approved by: https://github.com/jgong5, https://github.com/andrewor14
 2023-07-07 19:36:31 +00:00
Huy Do
f27a9129e7 XFAIL test_MaxUnpool_index_errors CUDA slow tests (#103905) 
This has been failing in trunk for a while.  Let's XFAIL it while continuing the investigation https://github.com/pytorch/pytorch/issues/103854.  We might not need this PR if the fix is on the way.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/103905
Approved by: https://github.com/mikaylagawarecki
 2023-06-22 18:05:10 +00:00
eqy
3f656ad7bb [CUDA] Do accumulation for Adaptive Average Pooling in opmath_t (#99378) 
Fix for an issue surfaced from the discuss forum: https://discuss.pytorch.org/t/adaptiveavgpool2d-causes-some-data-to-contain-inf/177420

CC @ptrblck @ngimel

Pull Request resolved: https://github.com/pytorch/pytorch/pull/99378
Approved by: https://github.com/ngimel
 2023-04-28 20:43:12 +00:00
puririshi98
8aa34602f7 Jetson Update for CI Redo (#94549) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94549
Approved by: https://github.com/ezyang, https://github.com/malfet
 2023-02-21 17:13:38 +00:00
Xuehai Pan
b005ec62b9 [BE] Remove dependency on six and future (#94709) 
Remove the Python 2 and 3 compatibility library [six](https://pypi.org/project/six) and [future](https://pypi.org/project/future) and `torch._six`. We only support Python 3.8+ now. It's time to retire them.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94709
Approved by: https://github.com/malfet, https://github.com/Skylion007
 2023-02-14 09:14:14 +00:00
Yuyao Wang
0bf78b57c0 fix: max_unpool3d buffer overflow (#94372) 
Fixes #88032

Previously `output_size` is accessed before the shape length check, which leads to a buffer overflow issue.

The fix is simply to prioritize the check.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94372
Approved by: https://github.com/albanD
 2023-02-08 19:48:25 +00:00
Khushi Agrawal
ccd8b66b0a [testing] add ErrorInputs for adaptive_{avg, max}_poolnd (#90924) 
Ref: https://github.com/pytorch/pytorch/pull/88906#discussion_r1040157313

Covers:
- [x] adaptive_avg_pool1d
- [x] adaptive_avg_pool2d
- [x] adaptive_avg_pool3d
- [x] adaptive_max_pool1d
- [x] adaptive_max_pool2d
- [x] adaptive_max_pool3d

Pull Request resolved: https://github.com/pytorch/pytorch/pull/90924
Approved by: https://github.com/mruberry
 2023-01-12 05:24:01 +00:00
Khushi Agrawal
7cd900eb97 [fix] adaptive_{avg, max}_pool variants : cuda & cpu (#88906) 
Fixes #78868

#### TODO
- [x] add tests
- [x] adaptive_avg_pool2d
- [x] adaptive_avg_pool3d
- [x] adaptive_max_pool2d
- [x] fix adaptive_max_pool3d_cuda

Pull Request resolved: https://github.com/pytorch/pytorch/pull/88906
Approved by: https://github.com/mruberry
 2022-12-13 20:57:00 +00:00
mingfeima
c6942dbbfb add shape check for random_samples in fractional_max_pool{2d|3d} (#89992) 
This PR add shape checks for `random_samples` in fractional_max_pool2d and fractional_max_pool3d.,
to provide more meaningful warnings instead of SegFault when the input is illegal.

For more details, please check https://github.com/pytorch/pytorch/issues/89648
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89992
Approved by: https://github.com/jgong5, https://github.com/ezyang
 2022-12-06 14:14:41 +00:00
Kshiteej K
ce856cee7e [test_nn] fix missing class attributes for NNTestCase (#89200) 
Missed setting these class variable 😓
Pull Request resolved: https://github.com/pytorch/pytorch/pull/89200
Approved by: https://github.com/albanD
 2022-11-22 22:55:44 +00:00
kshitij12345
8fb470e81a [fix] max_pool1d: shape check (#85594) 
Fixes #76587

Before PR:

```python
import torch
max_pool = torch.nn.MaxPool1d(3)
t = torch.rand([17, 0, 50], dtype=torch.float32)  # note requires_grad is False
max_pool(t) # Worked and returned tensor of shape [17, 0, 48].
```

After PR
```python
import torch
max_pool = torch.nn.MaxPool1d(3)
t = torch.rand([17, 0, 50], dtype=torch.float32)  # note requires_grad is False
max_pool(t) # Errors with `max_pool1d: Expected 2D or 3D (batch mode) tensor with optional 0 dim batch size for input, but got: [17, 0, 48]`
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85594
Approved by: https://github.com/mruberry
 2022-09-29 15:40:09 +00:00
Muhammed Shuaibi
4382da5d5e Remove assertEqualIgnoreType from test_pooling (#85112) 
Fix TODOs related to https://github.com/pytorch/pytorch/issues/38095 in test_pooling.py.

This PR correctly casts the expected outputs to satisfy the asserts. If you'd prefer feeding `exact_dtype=False` as an argument instead I can update accordingly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/85112
Approved by: https://github.com/kit1980
 2022-09-16 22:04:42 +00:00
Xiao Wang
5a29db142e Use int64_t index type in multiplications to avoid integer overflow in max_pool2d and avg_pool2d on CUDA (#68682) 
Fix https://github.com/pytorch/pytorch/issues/68418

- [X] operator benchmark: https://github.com/xwang233/code-snippet/tree/master/pooling-bench-68682, 10% or worse regression are seen in some shapes
- [X] end-to-end benchmark: no major regression seen in our test suites
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68682
Approved by: https://github.com/ngimel
 2022-09-12 22:45:38 +00:00
Aidyn-A
372a19d2c6 Update start_index and end_index for adaptive pooling (#84010) 
### Description
The PR fixes the issue #81409. To fix the issue the procedure of determining start and end indices for adaptive max pooling and average pooling is modified towards integer-only arithmetic.

### Testing
The testing of the new functions is straightforward:

```
#include <iostream>
#include <cassert>
#include <cmath>

int64_t start_index(int64_t a, int64_t b, int64_t c) {
  return (int64_t)std::floor((float)(a * c) / b);
}

int64_t end_index(int64_t a, int64_t b, int64_t c) {
  return (int64_t)std::ceil((float)((a + 1) * c) / b);
}

int64_t start_index_new(int64_t a, int64_t b, int64_t c) {
  return (a / b) * c + ((a % b) * c) / b;
}

int64_t end_index_new(int64_t a, int64_t b, int64_t c) {
  return 1 + ((a + 1) * c - 1) / b;
}

int main() {
    size_t N = 2<<24;
    std::cout<<N<<'\n';
    int64_t c = 1;

    for(int64_t i=1; i<N; i++) {
        for(int64_t j=1; j<N; j++) {
            int64_t s_id0 = start_index(i, j, c);
            int64_t s_id1 = start_index_new(i, j, c);
            assert(s_id0 == s_id1);
        }
    }

    for(int64_t i=1; i<N; i++) {
        for(int64_t j=1; j<N; j++) {
            int64_t e_id0 = end_index(i, j, c);
            int64_t e_id1 = end_index_new(i, j, c);
            assert(e_id0 == e_id1);
        }
    }
}
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/84010
Approved by: https://github.com/ezyang
 2022-08-29 22:53:40 +00:00