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Author SHA1 Message Date
PyTorch MergeBot
d578197747 Revert "Fix embedding jvp support by making embedding_renorm ignore forward mode AD (#78560)" 
This reverts commit ce7c7bb2a9.

Reverted https://github.com/pytorch/pytorch/pull/78560 on behalf of https://github.com/malfet due to broke XLA (on CI and trunk), see ce7c7bb2a9
 2022-06-02 17:40:34 +00:00
samdow
ce7c7bb2a9 Fix embedding jvp support by making embedding_renorm ignore forward mode AD (#78560) 
On functorch, we started seeing [embedding forward mode fail](https://github.com/pytorch/functorch/pull/816). From looking at it, we figured out that recently [embedding got forward mode support enabled](369d9f4137) and then doing forward mode with embedding and [max_norm doesn't work with gradcheck](https://github.com/pytorch/pytorch/blob/master/torch/testing/_internal/common_methods_invocations.py#L8877-L8881), so it's not checked.

What was happening is that `embedding_renorm` was setting `torch.no_grad()` which only turns off the backwards mode AD so functorch's jvp tests were still using forward mode AD during the `embedding_renorm` call. This makes it so that we don't use forward mode during the embedding_renorm call
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78560
Approved by: https://github.com/soulitzer, https://github.com/albanD
 2022-06-02 13:40:21 +00:00
Kshiteej K
4e1f41f66a [docs][nn] conv: complex support note (#78351) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/78351
Approved by: https://github.com/anjali411, https://github.com/jbschlosser
 2022-05-26 20:33:36 +00:00
Natalia Gimelshein
362525724b type promote clamp (#77035) 
Fixes #76630
When clamp(Tensor, Tensor) is structured, big parts of this PR won't be needed, but for now let's fix type promotion to make behavior more regular.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/77035
Approved by: https://github.com/mruberry
 2022-05-09 05:54:17 +00:00
vitrioil
f92cddd890 Removed direct doc formatting 
Fixes #76034

This does not make python remove all `__doc__` because in some places `__doc__` is assigned to a string.

Example:
04b3313379/torch/nn/modules/conv.py (L174-L233)

Since there are quite a few of these, I will add all of them together in this PR later. (Basically still a lot of docstring will persist even with `-OO` enabled.)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76619
Approved by: https://github.com/albanD
 2022-05-02 14:14:33 +00:00
Yuge Zhang
3ac27e78ca Fix typehint of multi_head_attention_forward 
Fixes #76169

Pull Request resolved: https://github.com/pytorch/pytorch/pull/76170
Approved by: https://github.com/jbschlosser
 2022-04-27 13:47:43 +00:00
Peter Bell
cb37e7a080 Remove F.pad python implementation 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73433

Approved by: https://github.com/albanD, https://github.com/jbschlosser
 2022-04-23 00:13:20 +00:00
vitrioil
29b004be7a Corrected documentation for supported padding 
Fixes #72521

Pull Request resolved: https://github.com/pytorch/pytorch/pull/76117
Approved by: https://github.com/jbschlosser
 2022-04-20 17:36:01 +00:00
Mike Ruberry
b09769992f Improves the OpInfo out= tests 
Edit: OpInfos separated into their own PRs to debug an ASAN failure that doesn't identify the failing test properly. This PR now just updates the out tests.

Adds OpInfos for:

- nn.functional.smooth_l1_loss
- nn.functional.l1_loss
- nn.functional.pdist
- nn.functional.binary_cross_entropy
- nn.functional.triplet_margin_loss
- nn.functional.triplet_margin_with_distance_loss
- nn.functional.max_unpool{1, 2, 3}D
- nn.functional.alpha_dropout
- nn.functional.soft_margin_loss
- nn.functional.multilabel_soft_margin_loss
- nn.functional.multilabel_margin_loss
- nn.functional.multi_margin_loss
- nn.functional.margin_ranking_loss

These OpInfos were taken from https://github.com/pytorch/pytorch/pull/67560, https://github.com/pytorch/pytorch/pull/67823, https://github.com/pytorch/pytorch/pull/68625, and https://github.com/pytorch/pytorch/pull/67079. The sample input update from https://github.com/pytorch/pytorch/pull/67017 is also rolled into this PR.

cc @zou3519 @nikitaved @pmeier @vfdev-5 @dagitses
Pull Request resolved: https://github.com/pytorch/pytorch/pull/75782
Approved by: https://github.com/ngimel
 2022-04-15 06:16:01 +00:00
Edward Z. Yang
0a1bc5f501 Miscellaneous __torch_function__ fixes 
I figured these out by unconditionally turning on a no-op torch function
mode on the test suite and then fixing errors as they showed up.  Here's
what I found:

- _parse_to failed internal assert when __torch_function__'ed because it
  claims its name is "to" to the argument parser; added a name override
  so we know how to find the correct name

- Infix operator magic methods on Tensor did not uniformly handle
  __torch_function__ and TypeError to NotImplemented.  Now, we always
  do the __torch_function__ handling in
  _wrap_type_error_to_not_implemented and your implementation of
  __torch_function__ gets its TypeErrors converted to NotImplemented
  (for better or for worse; see
  https://github.com/pytorch/pytorch/issues/75462 )

- A few cases where code was incorrectly testing if a Tensor was
  Tensor-like in the wrong way, now use is_tensor_like (in grad
  and in distributions).  Also update docs for has_torch_function to
  push people to use is_tensor_like.

- is_grads_batched was dropped from grad in handle_torch_function, now
  fixed

- Report that you have a torch function even if torch function is
  disabled if a mode is enabled.  This makes it possible for a mode
  to return NotImplemented, pass to a subclass which does some
  processing and then pass back to the mode even after the subclass
  disables __torch_function__ (so the tensors are treated "as if"
  they are regular Tensors).  This brings the C++ handling behavior
  in line with the Python behavior.

- Make the Python implementation of overloaded types computation match
  the C++ version: when torch function is disabled, there are no
  overloaded types (because they all report they are not overloaded).

Signed-off-by: Edward Z. Yang <ezyangfb.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/75484

Approved by: https://github.com/zou3519
 2022-04-11 16:52:16 +00:00
Scott Wolchok
87f40ee6d6 [PyTorch] Existing MHA: fuse the attn_mask addition (#73219) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73219

Saw a report that this elementwise add is causing overhead. IIUC this is easy to fuse?
ghstack-source-id: 152549975

Test Plan:
CI, review

Ran benchmark_transformers.par mha --batch-size 64 --max-sequence-length 128 --avg-sequence-length 256 --large --use-real-data-distribution --use-mask
and looked at the PT time number

```
before:
B=64, T=128, Half=True, GPU=True, Seed=1234, Padded tokens=54.92%, Use Mask=True             PT Time: 1.24ms, NativePT Time: 1000000000.00ms, HF Time: 1.10ms,             PT FLOPS: 59.07TFLOP/s, NativePT FLOPS: 0.00TFLOP/s, HF FLOPS: 66.46TFLOP/s
B=64, T=128, Half=True, GPU=True, Seed=1234, Padded tokens=54.92%, Use Mask=True             PT Time: 1.23ms, NativePT Time: 1000000000.00ms, HF Time: 1.09ms,             PT FLOPS: 59.57TFLOP/s, NativePT FLOPS: 0.00TFLOP/s, HF FLOPS: 66.75TFLOP/s
B=64, T=128, Half=True, GPU=True, Seed=1234, Padded tokens=54.92%, Use Mask=True             PT Time: 1.24ms, NativePT Time: 1000000000.00ms, HF Time: 1.09ms,             PT FLOPS: 58.87TFLOP/s, NativePT FLOPS: 0.00TFLOP/s, HF FLOPS: 66.77TFLOP/s

after:
B=64, T=128, Half=True, GPU=True, Seed=1234, Padded tokens=54.92%, Use Mask=True             PT Time: 1.22ms, NativePT Time: 1000000000.00ms, HF Time: 1.10ms,             PT FLOPS: 60.07TFLOP/s, NativePT FLOPS: 0.00TFLOP/s, HF FLOPS: 66.51TFLOP/s
B=64, T=128, Half=True, GPU=True, Seed=1234, Padded tokens=54.92%, Use Mask=True             PT Time: 1.22ms, NativePT Time: 1000000000.00ms, HF Time: 1.09ms,             PT FLOPS: 59.80TFLOP/s, NativePT FLOPS: 0.00TFLOP/s, HF FLOPS: 66.69TFLOP/s
B=64, T=128, Half=True, GPU=True, Seed=1234, Padded tokens=54.92%, Use Mask=True             PT Time: 1.21ms, NativePT Time: 1000000000.00ms, HF Time: 1.09ms,             PT FLOPS: 60.21TFLOP/s, NativePT FLOPS: 0.00TFLOP/s, HF FLOPS: 66.86TFLOP/s
```

Inspected a Kineto trace and confirmed that an elementwise add was fused into baddbmm.

Additional opportunity: I see a copy_ inside baddbmm that wasn't happening with the bmm path and I'm not sure why. Perhaps something went wrong with the structured kernels port by ezyang?

Reviewed By: ezyang

Differential Revision: D34160547

fbshipit-source-id: 78d406fb035e6f3bf13af2c9443a886eada35ac4
(cherry picked from commit aaffc39b24058742cb9ae42105f95b3eafe9d7f5)
 2022-04-04 20:31:22 +00:00
Peter Bell
7f051b4d2b Implement F.pad in ATen 
This moves the C++ torch pad function into ATen proper. Once the
forward-compatibility period is over, the python interface can use
this directly.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/73431

Approved by: https://github.com/ezyang
 2022-04-01 01:10:12 +00:00
Davit Kobaladze
8e12d2bf25 fixes torch.jit.script lp_pool bug. (#73287) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/60258

I used the solution proposed in https://github.com/pytorch/pytorch/issues/61275.  His solution failed unit tests and there was no progress after 08/07/2021. I'm willing to fix problems if they arise during CI.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/73287

Reviewed By: navahgar, zou3519

Differential Revision: D35057812

Pulled By: eellison

fbshipit-source-id: 8e82e9f73b9536979aecf476c5c65336cdffc93a
(cherry picked from commit e85e912a4edec1111623c5cbbba4171fe3bc5b1d)
 2022-03-28 23:16:07 +00:00
Peter Bell
f86bb2d6e4 Implement _pad_circular in ATen 
Closes #44459

This migrates the python implementation of `_pad_circular` to ATen and
removes the old C++ implementation that had diverged from python.

Note that `pad` can't actually use this until the
forward-compatibility period is over.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/73410

Approved by: https://github.com/ezyang
 2022-03-25 02:09:01 +00:00
Kushashwa Ravi Shrimali
452c26bbeb Fix functional.max_poolNd warning spam in the CI 
Fixes https://github.com/pytorch/pytorch/issues/71257.

Warnings have been removed, please see [this](https://github.com/pytorch/pytorch/pull/71258#issuecomment-1058503649) comment.

cc: @Lezcano @jbschlosser @zou3519
Pull Request resolved: https://github.com/pytorch/pytorch/pull/71258
Approved by: https://github.com/Lezcano, https://github.com/jbschlosser
 2022-03-04 18:42:23 +00:00
Scott Wolchok
28339ddc25 [PyTorch] Hit fused addmm path in linear() for existing MHA (#72871) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72871

We do this same trick in the native MHA implementation; backport it for purposes of fair comparison.
ghstack-source-id: 149526858

Test Plan: CI

Reviewed By: ngimel

Differential Revision: D34176090

fbshipit-source-id: 8b578c29c4dcf0d85bae74dfbbb82db9a8f32dc7
(cherry picked from commit fd50170935)
 2022-02-22 19:33:46 +00:00
Joel Schlosser
f670179c0a Fix doc regressions for various modules and functional forms (#73014) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/73014

Fixes #72501
Fixes #72502
Fixes #72503
Fixes #72504
Fixes #72505
Fixes #72506
Fixes #72507
Fixes #72509
Fixes #72510

Test Plan: Imported from OSS

Reviewed By: albanD

Differential Revision: D34305640

Pulled By: jbschlosser

fbshipit-source-id: 62f341633fdb0316eaa346cf7247865290eb830a
(cherry picked from commit 8362d264e7)
 2022-02-17 22:40:18 +00:00
Vitaly Fedyunin
81fbeea760 Add docstrings to native_channel_shuffle (#72919) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/72919

Test Plan: Imported from OSS

Reviewed By: bdhirsh

Differential Revision: D34274717

Pulled By: VitalyFedyunin

fbshipit-source-id: fa42f91ef2335e2594b19ef65d914c711f7a94fd
(cherry picked from commit a6f6fe9112)
 2022-02-17 02:33:08 +00:00
Ryan Spring
4f8b986e28 Implement Tanh Gelu Approximation (#61439) 
Summary:
1. Implements https://github.com/pytorch/pytorch/issues/39853
2. Adds approximate boolean flag to Gelu
3. Enables Tanh Gelu approximation
4. Adds double backward support for Gelu
5. Enable Tanh Gelu in NvFuser

```
def gelu(x, approximate : str = 'none'):
    if approximate == 'tanh':
        # sqrt(2/pi) = 0.7978845608028654
        return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * (x + 0.044715 * torch.pow(x, 3.0))))
    else:
        return x * normcdf(x)
```

Linking XLA PR - https://github.com/pytorch/xla/pull/3039

Pull Request resolved: https://github.com/pytorch/pytorch/pull/61439

Reviewed By: VitalyFedyunin

Differential Revision: D33894937

Pulled By: jbschlosser

fbshipit-source-id: b65e8fb6ea66168af8f34f45ed50e92737a33851
(cherry picked from commit 6e986f91a9)
 2022-02-14 03:40:32 +00:00
kshitij12345
02f6226bff [fix] Dropout2d-3d no-batch-dim (#69885) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/69801

TODO:
* [x] Update C++ API

cc albanD mruberry jbschlosser walterddr kshitij12345

Pull Request resolved: https://github.com/pytorch/pytorch/pull/69885

Reviewed By: mruberry

Differential Revision: D33175470

Pulled By: jbschlosser

fbshipit-source-id: c9d7d9e0f59ba290a0157725c338a345f3d58b9f
(cherry picked from commit 7e4271a156)
 2022-02-02 16:40:32 +00:00
pejato
b8a4ee5e35 Clean up old warnings in F.interpolate (#72093) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/71720

This PR removes the old warnings for `recompute_scale_factor` and `align_corners`.

Looking at this, I realize that the tests I modified don't really catch whether or not a warning is created for  `recompute_scale_factor`. If desired, I can add a couple lines into the tests there to pass a floating point in the `scale_factors` kwarg, along with `recompute_scale_factor=None`.

Let me know how this looks, thanks so much!

Pull Request resolved: https://github.com/pytorch/pytorch/pull/72093

Reviewed By: mruberry

Differential Revision: D33917615

Pulled By: albanD

fbshipit-source-id: e822f0a15b813ecf312cdc6ed0b693e7f1d1ca89
(cherry picked from commit c14852b85c)
 2022-02-01 21:18:29 +00:00
Peter Bell
e8d226cd9a Remove some unnecessary python functional wrappers (#61608) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61608

See #61544 for an example of issues created by functional wrappers. In this
case, these are directly wrapping the native function with no added
functionality. One exception was `bilinear` which was just missing the default
argument in C++, but was otherwise the same.

I've kept the symbol `torch.functional.istft` because it looks like public API,
but it could just as easily be moved to `_torch_docs.py`.

Test Plan: Imported from OSS

Reviewed By: ngimel

Differential Revision: D31401361

Pulled By: albanD

fbshipit-source-id: 162b74d0b2d4f2e5c4834687a94541960cefdd52
(cherry picked from commit 700cd73ca1)
 2022-02-01 16:59:26 +00:00
Nikita Shulga
74c44ba9d6 Revert D33850228: [pytorch][PR] Implement Tanh Gelu Approximation 
Test Plan: revert-hammer

Differential Revision:
D33850228 (23d03025dc)

Original commit changeset: 3cc33fb298e4

Original Phabricator Diff: D33850228 (23d03025dc)

fbshipit-source-id: 9436e7df73c2b2e2011f321674f24973316d3692
(cherry picked from commit c9efb58223)
 2022-01-31 17:44:19 +00:00
Ryan Spring
23d03025dc Implement Tanh Gelu Approximation (#61439) 
Summary:
1. Implements https://github.com/pytorch/pytorch/issues/39853
2. Adds approximate boolean flag to Gelu
3. Enables Tanh Gelu approximation
4. Adds double backward support for Gelu
5. Enable Tanh Gelu in NvFuser

```
def gelu(x, approximate : str = 'none'):
    if approximate == 'tanh':
        # sqrt(2/pi) = 0.7978845608028654
        return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * (x + 0.044715 * torch.pow(x, 3.0))))
    else:
        return x * normcdf(x)
```

Linking XLA PR - https://github.com/pytorch/xla/pull/3039

Pull Request resolved: https://github.com/pytorch/pytorch/pull/61439

Reviewed By: cpuhrsch

Differential Revision: D33850228

Pulled By: jbschlosser

fbshipit-source-id: 3cc33fb298e480d7ecc5c67716da019d60c6ab33
(cherry picked from commit 3a53b3e94f)
 2022-01-31 17:07:45 +00:00
vfdev
63429bf4b3 Removed JIT FC tweaks for interpolation options (#71937) 
Summary:
Description:
- Removed JIT FC tweaks for interpolation options : nearest-exact and antialiasing

They were added in
- https://github.com/pytorch/pytorch/pull/64501 (Sept 04 2021)
- https://github.com/pytorch/pytorch/pull/65142 (Sept 16 2021)

cc jbschlosser

Pull Request resolved: https://github.com/pytorch/pytorch/pull/71937

Reviewed By: mrshenli

Differential Revision: D33845502

Pulled By: jbschlosser

fbshipit-source-id: 8a94454fd643cd2aef21b06689f72a0f16620d30
(cherry picked from commit b21173d64c)
 2022-01-28 19:56:59 +00:00
Joel Schlosser
cb823d9f07 Revert D33744717: [pytorch][PR] Implement Tanh Gelu Approximation 
Test Plan: revert-hammer

Differential Revision:
D33744717 (f499ab9cef)

Original commit changeset: d64532a562ed

Original Phabricator Diff: D33744717 (f499ab9cef)

fbshipit-source-id: 396c3f63de5865f894dbc353d0790a01a624be93
(cherry picked from commit e9fb2d1db1)
 2022-01-28 18:35:01 +00:00
Ryan Spring
f499ab9cef Implement Tanh Gelu Approximation (#61439) 
Summary:
1. Implements https://github.com/pytorch/pytorch/issues/39853
2. Adds approximate boolean flag to Gelu
3. Enables Tanh Gelu approximation
4. Adds double backward support for Gelu
5. Enable Tanh Gelu in NvFuser

```
def gelu(x, approximate : str = 'none'):
    if approximate == 'tanh':
        # sqrt(2/pi) = 0.7978845608028654
        return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * (x + 0.044715 * torch.pow(x, 3.0))))
    else:
        return x * normcdf(x)
```

Linking XLA PR - https://github.com/pytorch/xla/pull/3039

Pull Request resolved: https://github.com/pytorch/pytorch/pull/61439

Reviewed By: mikaylagawarecki

Differential Revision: D33744717

Pulled By: jbschlosser

fbshipit-source-id: d64532a562ed53247bb4fa52bb16722634d5c187
(cherry picked from commit 4713dd9cca)
 2022-01-28 16:59:09 +00:00
kshitij12345
2981534f54 [nn] cross_entropy: no batch dim support (#71055) 
Summary:
Reference: https://github.com/pytorch/pytorch/issues/60585

cc albanD mruberry jbschlosser walterddr kshitij12345

Pull Request resolved: https://github.com/pytorch/pytorch/pull/71055

Reviewed By: anjali411

Differential Revision: D33567403

Pulled By: jbschlosser

fbshipit-source-id: 4d0a311ad7419387c4547e43e533840c8b6d09d8
 2022-01-13 14:48:51 -08:00
George Qi
d7db5fb462 ctc loss no batch dim support (#70092) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/70092

Test Plan: Imported from OSS

Reviewed By: jbschlosser

Differential Revision: D33280068

Pulled By: george-qi

fbshipit-source-id: 3278fb2d745a396fe27c00fb5f40df0e7f584f81
 2022-01-07 14:33:22 -08:00
Joel Schlosser
e6befbe85c Add flag to optionally average output attention weights across heads (#70055) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/47583

Pull Request resolved: https://github.com/pytorch/pytorch/pull/70055

Reviewed By: bhosmer

Differential Revision: D33457866

Pulled By: jbschlosser

fbshipit-source-id: 17746b3668b0148c1e1ed8333227b7c42f1e3bf5
 2022-01-06 17:32:37 -08:00
kshitij12345
1aa98c7540 [docs] multi_head_attention_forward no-batch dim support (#70590) 
Summary:
no batch dim support added in https://github.com/pytorch/pytorch/issues/67176

Pull Request resolved: https://github.com/pytorch/pytorch/pull/70590

Reviewed By: VitalyFedyunin

Differential Revision: D33405283

Pulled By: jbschlosser

fbshipit-source-id: 86217d7d540184fd12f3a9096605d2b1e9aa313e
 2022-01-05 08:26:25 -08:00
vfdev
d2abf3f981 Added antialias flag to interpolate (CPU only, bicubic) (#68819) 
Summary:
Description:
- Added antialias flag to interpolate (CPU only)
  - forward and backward for bicubic mode
  - added tests

Previous PR for bilinear, https://github.com/pytorch/pytorch/pull/65142

### Benchmarks

<details>
<summary>
Forward pass, CPU. PTH interpolation vs PIL
</summary>

Cases:
- PTH RGB 3 Channels, float32 vs PIL RGB uint8 (apples vs pears)
- PTH 1 Channel, float32 vs PIL 1 Channel Float

Code: https://gist.github.com/vfdev-5/b173761a567f2283b3c649c3c0574112

```
Torch config: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - CPU capability usage: AVX2
  - CUDA Runtime 11.1
  - NVCC architecture flags: -gencode;arch=compute_61,code=sm_61
  - CuDNN 8.0.5
  - Build settings: BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_PYTORCH_QNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.11.0, USE_CUDA=1, USE_CUDNN=1, USE_EIGEN_FOR_BLAS=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=OFF, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=0, USE_OPENMP=ON, USE_ROCM=OFF,

Num threads: 1
[------------------- Downsampling (bicubic): torch.Size([1, 3, 906, 438]) -> (320, 196) -------------------]
                                                  |  Reference, PIL 8.4.0, mode: RGB  |  1.11.0a0+gitb0bdf58
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                4.5                |          5.2
      channels_last non-contiguous torch.float32  |                4.5                |          5.3

Times are in milliseconds (ms).

[------------------- Downsampling (bicubic): torch.Size([1, 3, 906, 438]) -> (460, 220) -------------------]
                                                  |  Reference, PIL 8.4.0, mode: RGB  |  1.11.0a0+gitb0bdf58
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                5.7                |          6.4
      channels_last non-contiguous torch.float32  |                5.7                |          6.4

Times are in milliseconds (ms).

[------------------- Downsampling (bicubic): torch.Size([1, 3, 906, 438]) -> (120, 96) --------------------]
                                                  |  Reference, PIL 8.4.0, mode: RGB  |  1.11.0a0+gitb0bdf58
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                3.0                |          4.0
      channels_last non-contiguous torch.float32  |                2.9                |          4.1

Times are in milliseconds (ms).

[------------------ Downsampling (bicubic): torch.Size([1, 3, 906, 438]) -> (1200, 196) -------------------]
                                                  |  Reference, PIL 8.4.0, mode: RGB  |  1.11.0a0+gitb0bdf58
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                14.7               |          17.1
      channels_last non-contiguous torch.float32  |                14.8               |          17.2

Times are in milliseconds (ms).

[------------------ Downsampling (bicubic): torch.Size([1, 3, 906, 438]) -> (120, 1200) -------------------]
                                                  |  Reference, PIL 8.4.0, mode: RGB  |  1.11.0a0+gitb0bdf58
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                3.5                |          3.9
      channels_last non-contiguous torch.float32  |                3.5                |          3.9

Times are in milliseconds (ms).

[---------- Downsampling (bicubic): torch.Size([1, 1, 906, 438]) -> (320, 196) ---------]
                                 |  Reference, PIL 8.4.0, mode: F  |  1.11.0a0+gitb0bdf58
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               2.4               |          1.8

Times are in milliseconds (ms).

[---------- Downsampling (bicubic): torch.Size([1, 1, 906, 438]) -> (460, 220) ---------]
                                 |  Reference, PIL 8.4.0, mode: F  |  1.11.0a0+gitb0bdf58
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               3.1               |          2.2

Times are in milliseconds (ms).

[---------- Downsampling (bicubic): torch.Size([1, 1, 906, 438]) -> (120, 96) ----------]
                                 |  Reference, PIL 8.4.0, mode: F  |  1.11.0a0+gitb0bdf58
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               1.6               |          1.4

Times are in milliseconds (ms).

[--------- Downsampling (bicubic): torch.Size([1, 1, 906, 438]) -> (1200, 196) ---------]
                                 |  Reference, PIL 8.4.0, mode: F  |  1.11.0a0+gitb0bdf58
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               7.9               |          5.7

Times are in milliseconds (ms).

[--------- Downsampling (bicubic): torch.Size([1, 1, 906, 438]) -> (120, 1200) ---------]
                                 |  Reference, PIL 8.4.0, mode: F  |  1.11.0a0+gitb0bdf58
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               1.7               |          1.3

Times are in milliseconds (ms).

```

</details>

Code is moved from torchvision: https://github.com/pytorch/vision/pull/3810 and https://github.com/pytorch/vision/pull/4208

Pull Request resolved: https://github.com/pytorch/pytorch/pull/68819

Reviewed By: mikaylagawarecki

Differential Revision: D33339117

Pulled By: jbschlosser

fbshipit-source-id: 6a0443bbba5439f52c7dbc1be819b75634cf67c4
 2021-12-29 14:04:43 -08:00
srijan789
73b5b6792f Adds reduction args to signature of F.multilabel_soft_margin_loss docs (#70420) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/70301

Pull Request resolved: https://github.com/pytorch/pytorch/pull/70420

Reviewed By: gchanan

Differential Revision: D33336924

Pulled By: jbschlosser

fbshipit-source-id: 18189611b3fc1738900312efe521884bced42666
 2021-12-28 09:48:05 -08:00
George Qi
7c690ef1c2 FractionalMaxPool3d with no_batch_dim support (#69732) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/69732

Test Plan: Imported from OSS

Reviewed By: jbschlosser

Differential Revision: D33280090

Pulled By: george-qi

fbshipit-source-id: aaf90a372b6d80da0554bad28d56436676f9cb89
 2021-12-22 14:30:32 -08:00
rohitgr7
78bea1bb66 update example in classification losses (#69816) 
Summary:
Just updated a few examples that were either failing or raising deprecated warnings.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/69816

Reviewed By: bdhirsh

Differential Revision: D33217585

Pulled By: albanD

fbshipit-source-id: c6804909be74585c8471b8166b69e6693ad62ca7
 2021-12-21 02:46:48 -08:00
kshitij12345
e8d5c7cf7f [nn] mha : no-batch-dim support (python) (#67176) 
Summary:
Reference: https://github.com/pytorch/pytorch/issues/60585

* [x] Update docs
* [x] Tests for shape checking

Tests take roughly 20s on system that I use. Below is the timings for slowest 20 tests.

```
pytest test/test_modules.py -k _multih --durations=20
============================================================================================== test session starts ===============================================================================================
platform linux -- Python 3.10.0, pytest-6.2.5, py-1.10.0, pluggy-1.0.0
rootdir: /home/kshiteej/Pytorch/pytorch_no_batch_mha, configfile: pytest.ini
plugins: hypothesis-6.23.2, repeat-0.9.1
collected 372 items / 336 deselected / 36 selected

test/test_modules.py ..............ssssssss..............                                                                                                                                                  [100%]

================================================================================================ warnings summary ================================================================================================
../../.conda/envs/pytorch-cuda-dev/lib/python3.10/site-packages/torch/backends/cudnn/__init__.py:73
test/test_modules.py::TestModuleCUDA::test_factory_kwargs_nn_MultiheadAttention_cuda_float32
  /home/kshiteej/.conda/envs/pytorch-cuda-dev/lib/python3.10/site-packages/torch/backends/cudnn/__init__.py:73: UserWarning: PyTorch was compiled without cuDNN/MIOpen support. To use cuDNN/MIOpen, rebuild PyTorch making sure the library is visible to the build system.
    warnings.warn(

-- Docs: https://docs.pytest.org/en/stable/warnings.html
============================================================================================== slowest 20 durations ==============================================================================================
8.66s call     test/test_modules.py::TestModuleCUDA::test_gradgrad_nn_MultiheadAttention_cuda_float64
2.02s call     test/test_modules.py::TestModuleCPU::test_gradgrad_nn_MultiheadAttention_cpu_float64
1.89s call     test/test_modules.py::TestModuleCUDA::test_grad_nn_MultiheadAttention_cuda_float64
1.01s call     test/test_modules.py::TestModuleCUDA::test_factory_kwargs_nn_MultiheadAttention_cuda_float32
0.51s call     test/test_modules.py::TestModuleCPU::test_grad_nn_MultiheadAttention_cpu_float64
0.46s call     test/test_modules.py::TestModuleCUDA::test_forward_nn_MultiheadAttention_cuda_float32
0.45s call     test/test_modules.py::TestModuleCUDA::test_non_contiguous_tensors_nn_MultiheadAttention_cuda_float64
0.44s call     test/test_modules.py::TestModuleCUDA::test_non_contiguous_tensors_nn_MultiheadAttention_cuda_float32
0.21s call     test/test_modules.py::TestModuleCUDA::test_pickle_nn_MultiheadAttention_cuda_float64
0.21s call     test/test_modules.py::TestModuleCUDA::test_pickle_nn_MultiheadAttention_cuda_float32
0.18s call     test/test_modules.py::TestModuleCUDA::test_forward_nn_MultiheadAttention_cuda_float64
0.17s call     test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiheadAttention_cpu_float32
0.16s call     test/test_modules.py::TestModuleCPU::test_non_contiguous_tensors_nn_MultiheadAttention_cpu_float64
0.11s call     test/test_modules.py::TestModuleCUDA::test_factory_kwargs_nn_MultiheadAttention_cuda_float64
0.08s call     test/test_modules.py::TestModuleCPU::test_pickle_nn_MultiheadAttention_cpu_float32
0.08s call     test/test_modules.py::TestModuleCPU::test_pickle_nn_MultiheadAttention_cpu_float64
0.06s call     test/test_modules.py::TestModuleCUDA::test_repr_nn_MultiheadAttention_cuda_float64
0.06s call     test/test_modules.py::TestModuleCUDA::test_repr_nn_MultiheadAttention_cuda_float32
0.06s call     test/test_modules.py::TestModuleCPU::test_forward_nn_MultiheadAttention_cpu_float32
0.06s call     test/test_modules.py::TestModuleCPU::test_forward_nn_MultiheadAttention_cpu_float64
============================================================================================ short test summary info =============================================================================================
=========================================================================== 28 passed, 8 skipped, 336 deselected, 2 warnings in 19.71s ===========================================================================
```

cc albanD mruberry jbschlosser walterddr

Pull Request resolved: https://github.com/pytorch/pytorch/pull/67176

Reviewed By: dagitses

Differential Revision: D33094285

Pulled By: jbschlosser

fbshipit-source-id: 0dd08261b8a457bf8bad5c7f3f6ded14b0beaf0d
 2021-12-14 13:21:21 -08:00
Pearu Peterson
48771d1c7f [BC-breaking] Change dtype of softmax to support TorchScript and MyPy (#68336) 
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68336

Test Plan: Imported from OSS

Reviewed By: VitalyFedyunin

Differential Revision: D32470965

Pulled By: cpuhrsch

fbshipit-source-id: 254b62db155321e6a139bda9600722c948f946d3
 2021-11-18 11:26:14 -08:00
Richard Zou
f9ef807f4d Replace empty with new_empty in nn.functional.pad (#68565) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/68565

This makes it so that we can now vmap over nn.functional.pad (circular
variant). Previously we could not because we were effectively doing
`out.copy_(input)` where the out was created with empty.

This also has the added side effect of cleaning up the code.

Test Plan:
- I tested this using functorch.vmap and can confirm that vmap now
works.
- Unfortunately this doesn't work with the vmap in core so I cannot add
a test for this here.

Reviewed By: albanD

Differential Revision: D32520188

Pulled By: zou3519

fbshipit-source-id: 780a7e8207d7c45fcba645730a5803733ebfd7be
 2021-11-18 06:06:50 -08:00
vfdev-5
3da2e09c9b Added antialias flag to interpolate (CPU only, bilinear) (#65142) 
Summary:
Description:
- Added antialias flag to interpolate (CPU only)
  - forward and backward for bilinear mode
  - added tests

### Benchmarks

<details>
<summary>
Forward pass, CPU. PTH interpolation vs PIL
</summary>

Cases:
- PTH RGB 3 Channels, float32 vs PIL RGB uint8 (apply vs pears)
- PTH 1 Channel, float32 vs PIL 1 Channel Float

Code: https://gist.github.com/vfdev-5/b173761a567f2283b3c649c3c0574112

```
# OMP_NUM_THREADS=1 python bench_interp_aa_vs_pillow.py

Torch config: PyTorch built with:
  - GCC 9.3
  - C++ Version: 201402
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - CPU capability usage: AVX2
  - CUDA Runtime 11.1
  - NVCC architecture flags: -gencode;arch=compute_75,code=sm_75
  - CuDNN 8.0.5
  - Build settings: BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_PYTORCH_QNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=1, USE_CUDNN=1, USE_EIGEN_FOR_BLAS=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=OFF, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=0, USE_OPENMP=ON,

Num threads: 1
[------------------------ Downsampling: torch.Size([1, 3, 906, 438]) -> (320, 196) ------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                2.9                |          3.1
      channels_last non-contiguous torch.float32  |                2.6                |          3.6

Times are in milliseconds (ms).

[------------------------ Downsampling: torch.Size([1, 3, 906, 438]) -> (460, 220) ------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                3.4                |          4.0
      channels_last non-contiguous torch.float32  |                3.4                |          4.8

Times are in milliseconds (ms).

[------------------------ Downsampling: torch.Size([1, 3, 906, 438]) -> (120, 96) -------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                1.6                |          1.8
      channels_last non-contiguous torch.float32  |                1.6                |          1.9

Times are in milliseconds (ms).

[----------------------- Downsampling: torch.Size([1, 3, 906, 438]) -> (1200, 196) ------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                9.0                |          11.3
      channels_last non-contiguous torch.float32  |                8.9                |          12.5

Times are in milliseconds (ms).

[----------------------- Downsampling: torch.Size([1, 3, 906, 438]) -> (120, 1200) ------------------------]
                                                  |  Reference, PIL 8.3.2, mode: RGB  |  1.10.0a0+git1e87d91
1 threads: -------------------------------------------------------------------------------------------------
      channels_first contiguous torch.float32     |                2.1                |          1.8
      channels_last non-contiguous torch.float32  |                2.1                |          3.4

Times are in milliseconds (ms).

[--------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (320, 196) --------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               1.2               |          1.0

Times are in milliseconds (ms).

[--------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (460, 220) --------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               1.4               |          1.3

Times are in milliseconds (ms).

[--------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (120, 96) ---------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |              719.9              |         599.9

Times are in microseconds (us).

[-------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (1200, 196) --------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |               3.7               |          3.5

Times are in milliseconds (ms).

[-------------- Downsampling: torch.Size([1, 1, 906, 438]) -> (120, 1200) --------------]
                                 |  Reference, PIL 8.3.2, mode: F  |  1.10.0a0+git1e87d91
1 threads: ------------------------------------------------------------------------------
       contiguous torch.float32  |              834.4              |         605.7

Times are in microseconds (us).

```

</details>

Code is moved from torchvision: https://github.com/pytorch/vision/pull/4208

Pull Request resolved: https://github.com/pytorch/pytorch/pull/65142

Reviewed By: mrshenli

Differential Revision: D32432405

Pulled By: jbschlosser

fbshipit-source-id: b66c548347f257c522c36105868532e8bc1d4c6d
 2021-11-17 09:10:15 -08:00
vfdev-5
6adbe044e3 Added nearest-exact interpolation mode (#64501) 
Summary:
Added "nearest-exact" interpolation mode to fix the issues: https://github.com/pytorch/pytorch/issues/34808 and https://github.com/pytorch/pytorch/issues/62237.

Description:

As we can not fix "nearest" mode without large impact on already trained model [it was suggested](https://github.com/pytorch/pytorch/pull/64501#pullrequestreview-749771815) to introduce new mode instead of fixing exising "nearest" mode.

- New mode "nearest-exact" performs index computation for nearest interpolation to match scikit-image, pillow, TF2 and while "nearest" mode still match opencv INTER_NEAREST, which appears to be buggy, see https://ppwwyyxx.com/blog/2021/Where-are-Pixels/#Libraries.

"nearest":
```
input_index_f32 = output_index * scale
input_index = floor(input_index_f32)
```

"nearest-exact"
```
input_index_f32 = (output_index + 0.5) * scale - 0.5
input_index = round(input_index_f32)
```

Comparisions with other libs: https://gist.github.com/vfdev-5/a5bd5b1477b1c82a87a0f9e25c727664

PyTorch version | 1.9.0 "nearest" | this PR "nearest" | this PR "nearest-exact"
---|---|---|---
Resize option: | |
OpenCV INTER_NEAREST result mismatches | 0 | 0 | 10
OpenCV INTER_NEAREST_EXACT result mismatches | 9 | 9 | 9
Scikit-Image result mismatches | 10 | 10 | 0
Pillow result mismatches | 10 | 10 | 7
TensorFlow result mismatches | 10 | 10 | 0
Rescale option: | | |
size mismatches (https://github.com/pytorch/pytorch/issues/62396) | 10 | 10 | 10
OpenCV INTER_NEAREST result mismatches | 3 | 3| 5
OpenCV INTER_NEAREST_EXACT result mismatches | 3 | 3| 4
Scikit-Image result mismatches | 4 | 4 | 0
Scipy result mismatches | 4 | 4 | 0
TensorFlow: no such option | - |  -

Versions:
```
skimage: 0.19.0.dev0
opencv: 4.5.4-dev
scipy: 1.7.2
Pillow: 8.4.0
TensorFlow: 2.7.0
```

Implementations in other libs:

- Pillow:
  - ee079ae67e/src/libImaging/Geometry.c (L889-L899)
  - ee079ae67e/src/libImaging/Geometry.c (L11)
  - `a[2] == 0`

- Scikit-Image :
  - dev v0.19.0 uses scipy ndi.zoom:
    - 38fae50c3f/skimage/transform/_warps.py (L180-L188)
    - 47bb6febaa/scipy/ndimage/src/ni_interpolation.c (L775-L779)
    - 47bb6febaa/scipy/ndimage/src/ni_interpolation.c (L479)

Additionally:
- Updated upsampling tests

cc ezyang gchanan albanD mruberry jbschlosser walterddr fmassa heitorschueroff ppwwyyxx

Pull Request resolved: https://github.com/pytorch/pytorch/pull/64501

Reviewed By: anjali411

Differential Revision: D32361901

Pulled By: jbschlosser

fbshipit-source-id: df906f4d25a2b2180e1942ffbab2cc14600aeed2
 2021-11-15 14:28:19 -08:00
Junjie Wang
301369a774 [PyTorch][Fix] Pass the arguments of embedding as named arguments (#67574) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/67574

When adding the optional params for sharded embedding op. Found that we cannot get these params from `__torch_function__` override. The reason is that we don't pass them via keyword arguments. So maybe we want to change them to kwargs?
ghstack-source-id: 143029375

Test Plan: CI

Reviewed By: albanD

Differential Revision: D32039152

fbshipit-source-id: c7e598e49eddbabff6e11e3f8cb0818f57c839f6
 2021-11-11 15:22:10 -08:00
Kushashwa Ravi Shrimali
9e7b314318 OpInfo for nn.functional.conv1d (#67747) 
Summary:
This PR adds OpInfo for `nn.functional.conv1d`. There is a minor typo fix in the documentation as well.

Issue tracker: https://github.com/pytorch/pytorch/issues/54261

cc: mruberry

Pull Request resolved: https://github.com/pytorch/pytorch/pull/67747

Reviewed By: malfet

Differential Revision: D32309258

Pulled By: mruberry

fbshipit-source-id: add21911b8ae44413e033e19398f398210737c6c
 2021-11-11 09:23:04 -08:00
Natalia Gimelshein
8dfbc620d4 don't hardcode mask type in mha (#68077) 
Summary:
Fixes #{issue number}

Pull Request resolved: https://github.com/pytorch/pytorch/pull/68077

Reviewed By: zou3519

Differential Revision: D32292410

Pulled By: ngimel

fbshipit-source-id: 67213cf5474dc3f83e90e28cf5a823abb683a6f9
 2021-11-10 09:41:21 -08:00
vfdev-5
49bf24fc83 Updated error message for nn.functional.interpolate (#66417) 
Summary:
Description:
- Updated error message for nn.functional.interpolate

Fixes https://github.com/pytorch/pytorch/issues/63845

cc vadimkantorov

Pull Request resolved: https://github.com/pytorch/pytorch/pull/66417

Reviewed By: albanD

Differential Revision: D31924761

Pulled By: jbschlosser

fbshipit-source-id: ca74c77ac34b4f644aa10440b77b3fcbe4e770ac
 2021-10-26 10:33:24 -07:00
kshitij12345
828a9dcc04 [nn] MarginRankingLoss : no batch dim (#64975) 
Summary:
Reference: https://github.com/pytorch/pytorch/issues/60585

cc albanD mruberry jbschlosser walterddr

Pull Request resolved: https://github.com/pytorch/pytorch/pull/64975

Reviewed By: albanD

Differential Revision: D31906528

Pulled By: jbschlosser

fbshipit-source-id: 1127242a859085b1e06a4b71be19ad55049b38ba
 2021-10-26 09:03:31 -07:00
Mikayla Gawarecki
5569d5824c Fix documentation of arguments for torch.nn.functional.Linear (#66884) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/66884

Addressing docs fix mentioned in issue 64978 on Github
ghstack-source-id: 141093449

Test Plan: https://pxl.cl/1Rxkz

Reviewed By: anjali411

Differential Revision: D31767303

fbshipit-source-id: f1ca10fed5bb768749bce3ddc240bbce1dfb3f84
 2021-10-20 12:02:58 -07:00
vfdev
62ca5a81c0 Exposed recompute_scale_factor into nn.Upsample (#66419) 
Summary:
Description:
- Exposed recompute_scale_factor into nn.Upsample such that recompute_scale_factor=True option could be used

Context: https://github.com/pytorch/pytorch/pull/64501#discussion_r710205190

Pull Request resolved: https://github.com/pytorch/pytorch/pull/66419

Reviewed By: gchanan

Differential Revision: D31731276

Pulled By: jbschlosser

fbshipit-source-id: 2118489e6f5bc1142f2a64323f4cfd095a9f3c42
 2021-10-20 07:59:25 -07:00
kshitij12345
1db50505d5 [nn] MultiLabelSoftMarginLoss : no batch dim support (#65690) 
Summary:
Reference: https://github.com/pytorch/pytorch/issues/60585

cc albanD mruberry jbschlosser walterddr

Pull Request resolved: https://github.com/pytorch/pytorch/pull/65690

Reviewed By: zou3519

Differential Revision: D31731162

Pulled By: jbschlosser

fbshipit-source-id: d26f27555f78afdadd49126e0548a8bfda50cc5a
 2021-10-18 15:30:01 -07:00
Kushashwa Ravi Shrimali
909694fd88 Fix nn.functional.max_poolNd dispatch (for arg: return_indices) (#62544) 
Summary:
Please see https://github.com/pytorch/pytorch/issues/62545 for context.

The order of `return_indices, ceil_mode` is different for `nn.functional.max_poolNd` functions to what seen with `torch.nn.MaxPoolNd` (modular form). While this should be resolved in the future, it was decided to first raise a warning that the behavior will be changed in the future. (please see https://github.com/pytorch/pytorch/pull/62544#issuecomment-893770955 for more context)

This PR thus raises appropriate warnings and updates the documentation to show the full signature (along with a note) for `torch.nn.functional.max_poolNd` functions.

**Quick links:**

(_upstream_)

* Documentation of [`nn.functional.max_pool1d`](https://pytorch.org/docs/1.9.0/generated/torch.nn.functional.max_pool1d.html), [`nn.functional.max_pool2d`](https://pytorch.org/docs/stable/generated/torch.nn.functional.max_pool2d.html), and [`nn.functional.max_pool3d`](https://pytorch.org/docs/stable/generated/torch.nn.functional.max_pool3d.html).

(_this branch_)

* Documentation of [`nn.functional.max_pool1d`](https://docs-preview.pytorch.org/62544/generated/torch.nn.functional.max_pool1d.html?highlight=max_pool1d), [`nn.functional.max_pool2d`](https://docs-preview.pytorch.org/62544/generated/torch.nn.functional.max_pool2d.html?highlight=max_pool2d#torch.nn.functional.max_pool2d), and [`nn.functional.max_pool3d`](https://docs-preview.pytorch.org/62544/generated/torch.nn.functional.max_pool3d.html?highlight=max_pool3d#torch.nn.functional.max_pool3d).

cc mruberry jbschlosser

Pull Request resolved: https://github.com/pytorch/pytorch/pull/62544

Reviewed By: gchanan

Differential Revision: D31179038

Pulled By: jbschlosser

fbshipit-source-id: 0a2c7215df9e132ce9ec51448c5b3c90bbc69030
 2021-10-18 08:34:38 -07:00
Natalia Gimelshein
4a50b6c490 fix cosine similarity dimensionality check (#66191) 
Summary:
Fixes https://github.com/pytorch/pytorch/issues/66086

Pull Request resolved: https://github.com/pytorch/pytorch/pull/66191

Reviewed By: dagitses, malfet

Differential Revision: D31436997

Pulled By: ngimel

fbshipit-source-id: 363556eea4e1696d928ae08320d298451c286b10
 2021-10-06 15:44:51 -07:00