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Author SHA1 Message Date
PyTorch MergeBot
cfec69e2a1 Revert "Update fused kernels and call _safe_softmax from SDPA (#131863)" 
This reverts commit caba37e99b.

Reverted https://github.com/pytorch/pytorch/pull/131863 on behalf of https://github.com/izaitsevfb due to breaks executorch test executorch/backends/apple/coreml:test - test_vit_skip_conv (executorch.backends.apple.coreml.test.test_coreml_partitioner.TestCoreMLPartitioner) ([comment](https://github.com/pytorch/pytorch/pull/131863#issuecomment-2291855634))
 2024-08-15 17:55:07 +00:00
drisspg
caba37e99b Update fused kernels and call _safe_softmax from SDPA (#131863) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131863
Approved by: https://github.com/jbschlosser, https://github.com/Chillee
 2024-08-13 23:37:50 +00:00
yuqingj
623d0204f0 [NJT] Support Chunk backward for simple cases (#132193) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132193
Approved by: https://github.com/soulitzer
 2024-08-06 21:20:09 +00:00
Joel Schlosser
a356a03f4a Fix DEBUG=1 asserts for mvlgamma backward with NJT (#132422) 
mvlgamma backward trips DEBUG=1 asserts when trying to construct an empty tensor with `layout=torch.jagged`. This happens due to passing `self.options()` to `arange()` in `mvlgamma_backward()`. Fix in this PR unconditionally constructs `arange()` with the strided layout.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/132422
Approved by: https://github.com/albanD
 2024-08-01 21:53:16 +00:00
cyy
798b9652f7 [6/N] Replace c10::optional with std::optional (#130438) 
Follows #130408

Pull Request resolved: https://github.com/pytorch/pytorch/pull/130438
Approved by: https://github.com/janeyx99
 2024-07-11 01:15:37 +00:00
cyy
f4dcf2ae93 [1/N] Change #include <c10/util/Optional.h> to #include <optional> (#128301) 
Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128301
Approved by: https://github.com/ezyang, https://github.com/r-barnes
 2024-07-08 07:03:53 +00:00
Joel Schlosser
e1c1052829 Backward support for unbind() with NJT (#128032) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128032
Approved by: https://github.com/soulitzer
 2024-06-21 14:05:23 +00:00
PyTorch MergeBot
5ffb032be6 Revert "Backward support for unbind() with NJT (#128032)" 
This reverts commit 5dc4f652bc.

Reverted https://github.com/pytorch/pytorch/pull/128032 on behalf of https://github.com/jbschlosser due to reverting to revert parent PR ([comment](https://github.com/pytorch/pytorch/pull/128032#issuecomment-2177296325))
 2024-06-19 00:26:40 +00:00
Joel Schlosser
5dc4f652bc Backward support for unbind() with NJT (#128032) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/128032
Approved by: https://github.com/soulitzer
 2024-06-18 20:29:00 +00:00
PyTorch MergeBot
846bb30e13 Revert "[1/N] Change #include <c10/util/Optional.h> to #include <optional> (#128301)" 
This reverts commit bd72e28314.

Reverted https://github.com/pytorch/pytorch/pull/128301 on behalf of https://github.com/huydhn due to Sorry for reverting your change but it fails XLA build bd72e28314. Please rebase your PR before relanding because I think the failure is hidden by an unrelated broken trunk XLA failure from your current base commit ([comment](https://github.com/pytorch/pytorch/pull/128301#issuecomment-2169035822))
 2024-06-15 01:58:20 +00:00
cyy
bd72e28314 [1/N] Change #include <c10/util/Optional.h> to #include <optional> (#128301) 
Fixes #ISSUE_NUMBER

Pull Request resolved: https://github.com/pytorch/pytorch/pull/128301
Approved by: https://github.com/ezyang
 2024-06-14 23:21:01 +00:00
lezcano
00999fd8b1 Prefer flip over index_select (#126783) 
It's faster and has a lower memory footprint in eager.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/126783
Approved by: https://github.com/Skylion007, https://github.com/albanD
ghstack dependencies: #114471
 2024-05-29 09:10:25 +00:00
Richard Barnes
ed327876f5 [codemod] c10:optional -> std::optional (#126135) 
Generated by running the following from PyTorch root:
```
find . -regex ".*\.\(cpp\|h\|cu\|hpp\|cc\|cxx\)$" | grep -v "build/" | xargs -n 50 -P 4 perl -pi -e 's/c10::optional/std::optional/'
```

`c10::optional` is just an alias for `std::optional`. This removes usages of that alias in preparation for eliminating it entirely.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/126135
Approved by: https://github.com/Skylion007, https://github.com/malfet, https://github.com/albanD, https://github.com/aaronenyeshi
 2024-05-14 19:35:51 +00:00
Guilherme Leobas
761a7b84ba [Dynamo] Fix alias issue with respect to wrapped numbers (#124731) (#124774) 
This PR fixes an issue presented when calling `aten.alias(int)` raises a TypeError.

```python
import torch
import torch.autograd.forward_ad as fwAD

def f(x):
    return 4312491 * x

device = "cpu"

with torch._subclasses.fake_tensor.FakeTensorMode():
    with fwAD.dual_level():
        x = torch.randn(3, device=device)
        y = torch.ones_like(x)
        dual = fwAD.make_dual(x, y)
        f(dual)
```

The test case above illustrates this bug.
1) `4312491` turns into a tensor that is a wrapped number
2) Forward mode AD calls `aten::alias` internally
3) The wrapped number (`4312491`) becomes a python integer
4) `aten.alias(int)` raises a `TypeError`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/124774
Approved by: https://github.com/albanD, https://github.com/zou3519
 2024-04-30 14:11:46 +00:00
Brian Hirsh
2fe672b146 compile: ban mutations on non-compositional uses of as_strided (#122502) 
Fixes https://github.com/pytorch/pytorch/issues/104505

I was originally going to ban all usages of as_strided + mutation in functionalization. But I'm pretty sure that as_strided + mutation is fine when we are calling as_strided on a base tensor.

So in this PR I added a slightly more conservative check: if we see an as_strided + mutation, where the input to an as_strided was **another** view op, then I error loudly in functionalization and link to the github issue above (in case anyone runs into this in the real world)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/122502
Approved by: https://github.com/ezyang, https://github.com/albanD
 2024-04-12 01:12:23 +00:00
Guilherme Leobas
7ffad9ab04 Use out-of-place version of put inside take_backward (#123268) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/123268
Approved by: https://github.com/zou3519
ghstack dependencies: #122211, #122212, #122213
 2024-04-05 03:29:11 +00:00
Flavio Sales Truzzi
7fb2d69282 [PT2] - Fix cat backwards wrapping on symints (#121527) 
Summary:
Wrapping was comparing Symint and ints forcing a guard. Rewrite it with TORCH_GUARD_SIZE_OBLIVIOUS
```
[trainer0|0]:  File "<invalid>", line 0, in THPEngine_run_backward(_object*, _object*, _object*)
[trainer0|0]:  File "<invalid>", line 0, in torch::autograd::python::PythonEngine::execute(std::vector<torch::autograd::Edge, std::allocator<torch::autograd::Edge>> const&, std::vector<at::Tensor, std::allocator<at::Tensor>> const&, bool, bool, bool, std::vector<torch::autograd::Edge, std::allocator<torch::autograd::Edge>> const&)
[trainer0|0]:  File "<invalid>", line 0, in torch::autograd::Engine::execute(std::vector<torch::autograd::Edge, std::allocator<torch::autograd::Edge>> const&, std::vector<at::Tensor, std::allocator<at::Tensor>> const&, bool, bool, bool, std::vector<torch::autograd::Edge, std::allocator<torch::autograd::Edge>> const&)
[trainer0|0]:  File "<invalid>", line 0, in torch::autograd::python::PythonEngine::execute_with_graph_task(std::shared_ptr<torch::autograd::GraphTask> const&, std::shared_ptr<torch::autograd::Node>, torch::autograd::InputBuffer&&)
[trainer0|0]:  File "<invalid>", line 0, in torch::autograd::Engine::execute_with_graph_task(std::shared_ptr<torch::autograd::GraphTask> const&, std::shared_ptr<torch::autograd::Node>, torch::autograd::InputBuffer&&)
[trainer0|0]:  File "<invalid>", line 0, in torch::autograd::Engine::thread_main(std::shared_ptr<torch::autograd::GraphTask> const&)
[trainer0|0]:  File "<invalid>", line 0, in torch::autograd::Engine::evaluate_function(std::shared_ptr<torch::autograd::GraphTask>&, torch::autograd::Node*, torch::autograd::InputBuffer&, std::shared_ptr<torch::autograd::ReadyQueue> const&)
[trainer0|0]:  File "<invalid>", line 0, in torch::autograd::Node::operator()(std::vector<at::Tensor, std::allocator<at::Tensor>>&&)
[trainer0|0]:  File "<invalid>", line 0, in torch::autograd::generated::CatBackward0::apply(std::vector<at::Tensor, std::allocator<at::Tensor>>&&)
[trainer0|0]:  File "<invalid>", line 0, in torch::autograd::generated::details::cat_tensors_backward(at::Tensor const&, std::vector<std::vector<c10::SymInt, std::allocator<c10::SymInt>>, std::allocator<std::vector<c10::SymInt, std::allocator<c10::SymInt>>>> const&, std::vector<c10::ScalarType, std::allocator<c10::ScalarType>> const&, long)
[trainer0|0]:  File "<invalid>", line 0, in c10::operator==(c10::SymInt const&, int)
[trainer0|0]:  File "<invalid>", line 0, in c10::SymBool::guard_bool(char const*, long) const
[trainer0|0]:  File "<invalid>", line 0, in torch::impl::PythonSymNodeImpl::guard_bool(char const*, long)
```

Test Plan: Regular CI

Differential Revision: D54667300

Pull Request resolved: https://github.com/pytorch/pytorch/pull/121527
Approved by: https://github.com/ezyang
 2024-03-19 18:03:02 +00:00
Edward Z. Yang
0a7666801d SymIntify prod_backward (#120776) 
Fixes https://github.com/pytorch/pytorch/issues/120608

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

Pull Request resolved: https://github.com/pytorch/pytorch/pull/120776
Approved by: https://github.com/albanD
 2024-02-29 20:05:22 +00:00
cyy
87c6cd2f00 [1/N] Replace std::tie with structural binding (#119774) 
This PR replaces some std::tie calls with structural binding from C++17.  This not only makes the code more compact, but also has some performance gain.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/119774
Approved by: https://github.com/albanD, https://github.com/malfet
 2024-02-14 09:25:04 +00:00
Peter Bell
08657b82f5 Reduce scope of dispatching in logcumsumexp_backward (#119397) 
Everything inside the `AT_DISPATCH` block is being compiled 5 times,
so it makes sense to limit it to the only line that uses `scalar_t` which is
the `numeric_limits` query.

Also a small optimization, instead of computing `grad.log()` and `(-grad).log()`
we can compute `grad.abs().log()` which is 2 pointwise ops instead of 3.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/119397
Approved by: https://github.com/lezcano, https://github.com/albanD
 2024-02-08 15:09:22 +00:00
Pearu Peterson
34fe850d00 SymInt'ify sparse_compressed_tensor (#107903) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107903
Approved by: https://github.com/cpuhrsch
ghstack dependencies: #115586
 2023-12-17 17:36:20 +00:00
Pearu Peterson
194d57dae7 Add values backward support for sparse CSR, CSC, BSR, and BSC tensors (#115586) 
Fixes https://github.com/pytorch/pytorch/issues/107286

Pull Request resolved: https://github.com/pytorch/pytorch/pull/115586
Approved by: https://github.com/cpuhrsch, https://github.com/albanD
 2023-12-14 23:09:13 +00:00
Scott Wolchok
165f4f6ccf [PyTorch] Redirect c10::optional to std::optional (#101995) 
We have C++17 now!

I am intentionally dropping the `c10::optional<c10::ArrayRef>` size optimization. It was intended to improve dispatch, but thanks to D34602980 / #70864 we don't use `optional<ArrayRef>` in function arguments anymore anyway.

Differential Revision: [D46079028](https://our.internmc.facebook.com/intern/diff/D46079028/)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/101995
Approved by: https://github.com/malfet, https://github.com/Skylion007, https://github.com/ezyang
 2023-11-30 02:46:41 +00:00
Guilherme Leobas
77f16eb00c Fix prod double backward when there are 2+ zeros (#113969) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/113969
Approved by: https://github.com/albanD
 2023-11-21 01:32:10 +00:00
Edward Z. Yang
8c4812be80 Replace expect_int with guard_int (#113921) 
The idea is that instead of erroring, we will just specialize at these sites.

Fixes https://github.com/pytorch/pytorch/issues/113142

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

Pull Request resolved: https://github.com/pytorch/pytorch/pull/113921
Approved by: https://github.com/zou3519
 2023-11-20 21:27:48 +00:00
Miles Lubin
070b2d3cff cholesky_solve_backward: speed up using output_mask (#112981) 
Introduces a faster path for `cholesky_solve_backward` when the gradient with respect to the cholesky factor isn't required.

Adds test coverage in `test_linalg.py`.

# Example

## Setup

```py
import torch
torch.set_num_threads(1)
mat = torch.randn(500, 1000)
mat = mat @ mat.T
L = torch.linalg.cholesky(mat, upper=False)

rhs = torch.randn(500, 1)
rhs.requires_grad = True

sol = torch.cholesky_solve(rhs, L, upper=False).sum(dim=0)
```

## Before
```
%timeit torch.autograd.grad(sol, rhs, retain_graph=True)
2.61 ms ± 18.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```

## After
```
%timeit torch.autograd.grad(sol, rhs, retain_graph=True)
109 µs ± 3.42 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/112981
Approved by: https://github.com/lezcano
 2023-11-16 18:30:57 +00:00
eellison
325e0fdfdd Enable masked_scatter_backward for inductor (#109642) 
masked_scatter_backward was previously implemented as a
CompositeExplicitAutograd, which involved a decomp that calls
masked_select, and masked_select in general produces data-dependent
shapes that inductor doesn't support. But masked_scatter_backward
reshapes the return value of masked_select such that the end result has
a static shape again.

I have converted masked_scatter_backward into an aten op to avoid this
issue.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/109642
Approved by: https://github.com/ezyang
ghstack dependencies: #108170
 2023-11-09 01:27:57 +00:00
Edward Z. Yang
c84c86f018 SymIntify convolution (#111599) 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>

Pull Request resolved: https://github.com/pytorch/pytorch/pull/111599
Approved by: https://github.com/wanchaol, https://github.com/bdhirsh
 2023-10-21 03:03:20 +00:00
Joel Schlosser
17348b0f51 Implement split_with_sizes backward for NT (#110647) 
Needed internally. Note that `split_with_sizes()` for NT is currently supported only on `dim=-1`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110647
Approved by: https://github.com/cpuhrsch, https://github.com/soulitzer
ghstack dependencies: #110646
 2023-10-06 18:44:22 +00:00
Joel Schlosser
48240ec62e Make unbind() overrideable for NT subclass (#110646) 
Reland of #109122. Fixed the memory leak by not saving the outputs of `unbind()` for backward. Rather, the NT sizes are saved so undefined grads can replaced with zeros of the correct size.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/110646
Approved by: https://github.com/soulitzer, https://github.com/cpuhrsch
 2023-10-06 18:44:22 +00:00
Li-Huai (Allan) Lin
a3c1e3c95c Generalize toAccumulateType() (#108248) 
Trying to address this comment: https://github.com/pytorch/pytorch/pull/106666#discussion_r1297397554

Pull Request resolved: https://github.com/pytorch/pytorch/pull/108248
Approved by: https://github.com/kulinseth, https://github.com/albanD
 2023-10-02 16:34:36 +00:00
PyTorch MergeBot
b083058e45 Revert "Make unbind() overrideable for NT subclass (#109122)" 
This reverts commit f5a23ca78d.

Reverted https://github.com/pytorch/pytorch/pull/109122 on behalf of https://github.com/PaliC due to breaking slow tests ([comment](https://github.com/pytorch/pytorch/pull/109122#issuecomment-1741555305))
 2023-09-29 22:41:56 +00:00
Joel Schlosser
f5a23ca78d Make unbind() overrideable for NT subclass (#109122) 
Goal: avoid making unbind composite implicit so we can override it within `__torch_dispatch__()` for the NT subclass.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109122
Approved by: https://github.com/cpuhrsch, https://github.com/soulitzer
 2023-09-28 01:26:22 +00:00
cyy
51d2d825ab [3/N] apply clang-tidy in torch/csrc/autograd (#109368) 
This PR applies clang-tidy fixes in torch/csrc/autograd/FunctionsManual.cpp. There are also other fixes.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/109368
Approved by: https://github.com/Skylion007
 2023-09-17 07:26:59 +00:00
cyy
a14d30d8d1 [1/N] apply clang-tidy in torch/csrc/autograd (#109032) 
This PR begins a new series of patches for enabling clang-tidy checks in torch/csrc/augograd
Pull Request resolved: https://github.com/pytorch/pytorch/pull/109032
Approved by: https://github.com/albanD, https://github.com/Skylion007
 2023-09-15 23:28:43 +00:00
David Berard
d465d6a838 [inductor] scatter_reduce - skip .item() in backward if GradMode is not enabled (#107353) 
Repeats #106429 for scatter_reduce so that the backward will pass for PT2. The .item() call is only needed to make double-backward work, which isn't supported anyway for PT2; so an easy fix is to just skip the .item() call if we know we won't need double-backward.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/107353
Approved by: https://github.com/eellison
 2023-08-18 07:17:29 +00:00
arunppsg
4bfc55ba8b [MPS] Enable forward test for renorm (#106666) 
Enabled forward test for renorm

Pull Request resolved: https://github.com/pytorch/pytorch/pull/106666
Approved by: https://github.com/kulinseth, https://github.com/albanD
 2023-08-17 16:46:06 +00:00
David Berard
393e9eed90 [inductor] modify index_reduce to pass opinfo tests (#106429) 
1. add a python meta registration, to fix an issue with the forward pass. The problem was that previously, the C++ meta registration calls [numel()](7b14a14e27/aten/src/ATen/native/TensorAdvancedIndexing.cpp (L329)) which fails (LMK if it's better to fix the C++ implementation to not do this check)
2. Modify the backward to fix an issue in the backward. The backward is not a custom op - it's a custom manual backward implementation. In particular, there's some situations that don't support double backward; the check for whether double backward is allowed requires a .item() call. To fix the meta/fake tensor case, this PR will avoid setting the double backward error only if `GradMode::is_enabled()` - which shouldn't be turned on in PT2.
3. Update skips.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/106429
Approved by: https://github.com/zou3519
 2023-08-10 18:14:00 +00:00
Nikita Vedeneev
437bc5b1b7 sparse_mask: backward support for sparse lhs (take 2) (#104341) 
This is a copy of https://github.com/pytorch/pytorch/pull/95165 with some bug fixes.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/104341
Approved by: https://github.com/albanD, https://github.com/pearu, https://github.com/amjames
 2023-07-03 14:12:44 +00:00
Nikita Karetnikov
c4a6f86062 [pt2] add metas for max_unpool2d and max_unpool3d (#103821) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103821
Approved by: https://github.com/Skylion007, https://github.com/Chillee
 2023-07-01 01:33:35 +00:00
Nikita Vedeneev
5cf3a99013 sampled_addmm: backward performance improvements (#103544) 
No need to do double `sparse_mask`, let's squash everything into one call!
This PR exercises https://github.com/pytorch/pytorch/pull/103750, so here is an autogened code for the backward pass.

```
at::Tensor sparse_sampled_addmm(c10::DispatchKeySet ks, const at::Tensor & self, const at::Tensor & mat1, const at::Tensor & mat2, const at::Scalar & beta, const at::Scalar & alpha) {
  auto& self_ = unpack(self, "self", 0);
  auto& mat1_ = unpack(mat1, "mat1", 1);
  auto& mat2_ = unpack(mat2, "mat2", 2);
  [[maybe_unused]] auto _any_requires_grad = compute_requires_grad( self, mat1, mat2 );

  std::shared_ptr<SparseSampledAddmmBackward0> grad_fn;
  if (_any_requires_grad) {
    grad_fn = std::shared_ptr<SparseSampledAddmmBackward0>(new SparseSampledAddmmBackward0(), deleteNode);
    grad_fn->set_next_edges(collect_next_edges( self, mat1, mat2 ));
    grad_fn->alpha = alpha;
    grad_fn->beta = beta;
    if (grad_fn->should_compute_output(2)) {
      grad_fn->mat1_ = SavedVariable(mat1, false);
    }
    if (grad_fn->should_compute_output(1)) {
      grad_fn->mat2_ = SavedVariable(mat2, false);
    }
    grad_fn->self_ = SavedVariable(self, false);
  }

```

As you can see, we do not save tensors unless needed.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/103544
Approved by: https://github.com/soulitzer
 2023-06-28 08:49:54 +00:00
Nikita Vedeneev
567b5e5b28 Multioutput backward formula: allow conditional guards against saving (#103750) 
Multi-output backward formulas break the ability of autogen to decide which variables have to be stored in a graph.
This PR introduces a macro `wrap_opt_if` which could be used to hint autogen about variable interdependence.

For example, the following code is being generated for `_trilinear` with this modification:
```
at::Tensor _trilinear(c10::DispatchKeySet ks, const at::Tensor & i1, const at::Tensor & i2, const at::Tensor & i3, at::IntArrayRef expand1, at::IntArrayRef expand2, at::IntArrayRef expand3, at::IntArrayRef sumdim, int64_t unroll_dim) {
  auto& i1_ = unpack(i1, "i1", 0);
  auto& i2_ = unpack(i2, "i2", 1);
  auto& i3_ = unpack(i3, "i3", 2);
  [[maybe_unused]] auto _any_requires_grad = compute_requires_grad( i1, i2, i3 );

  [[maybe_unused]] auto _any_has_forward_grad_result = (isFwGradDefined(i1) || isFwGradDefined(i2) || isFwGradDefined(i3));
  std::shared_ptr<TrilinearBackward0> grad_fn;
  if (_any_requires_grad) {
    grad_fn = std::shared_ptr<TrilinearBackward0>(new TrilinearBackward0(), deleteNode);
    grad_fn->set_next_edges(collect_next_edges( i1, i2, i3 ));
    grad_fn->expand1 = expand1.vec();
    grad_fn->expand2 = expand2.vec();
    grad_fn->expand3 = expand3.vec();
    if (grad_fn->should_compute_output(1) || grad_fn->should_compute_output(2)) {
      grad_fn->i1_ = SavedVariable(i1, false);
    }
    if (grad_fn->should_compute_output(0) || grad_fn->should_compute_output(2)) {
      grad_fn->i2_ = SavedVariable(i2, false);
    }
    if (grad_fn->should_compute_output(0) || grad_fn->should_compute_output(1)) {
      grad_fn->i3_ = SavedVariable(i3, false);
    }
    grad_fn->sumdim = sumdim.vec();
  }

```

with the following backward modifications:
```
 - name: _trilinear(Tensor i1, Tensor i2, Tensor i3, int[] expand1, int[] expand2, int[] expand3, int[] sumdim, int unroll_dim=1) -> Tensor
  - i1, i2, i3: _trilinear_backward(grad, i1, i2, i3, expand1, expand2, expand3, sumdim, grad_input_mask)
  + i1, i2, i3: "_trilinear_backward(grad,
  +             wrap_opt_if(i1, grad_input_mask[1] || grad_input_mask[2]),
  +             wrap_opt_if(i2, grad_input_mask[0] || grad_input_mask[2]),
  +             wrap_opt_if(i3, grad_input_mask[0] || grad_input_mask[1]),
  +             expand1, expand2, expand3, sumdim, grad_input_mask)"
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/103750
Approved by: https://github.com/soulitzer
 2023-06-27 15:12:09 +00:00
cyy
d4a98280a8 [Reland] Use missing-prototypes in torch_cpu (#104138) 
This PR enables Wmissing-prototypes in torch_cpu except some generated cpp files and the mps and metal,vulkan backends and caffe2 sources.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/104138
Approved by: https://github.com/albanD, https://github.com/malfet
 2023-06-26 22:53:43 +00:00
PyTorch MergeBot
7274582390 Revert "sparse_mask: backward support for sparse lhs (#95165)" 
This reverts commit f090fdf3b4.

Reverted https://github.com/pytorch/pytorch/pull/95165 on behalf of https://github.com/huydhn due to Sorry for reverting this. I think one of the tests test_sparse.py::TestSparseCUDA::test_sparse_mask_backward_cuda_complex128 is failing on slow gradcheck f090fdf3b4 ([comment](https://github.com/pytorch/pytorch/pull/95165#issuecomment-1604696109))
 2023-06-23 18:40:15 +00:00
Nikita Vedeneev
f090fdf3b4 sparse_mask: backward support for sparse lhs (#95165) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/95165
Approved by: https://github.com/pearu, https://github.com/cpuhrsch
 2023-06-23 12:27:27 +00:00
PyTorch MergeBot
b5594f7df0 Revert "Use missing-prototypes in torch_cpu (#103725)" 
This reverts commit 716b3b893d.

Reverted https://github.com/pytorch/pytorch/pull/103725 on behalf of https://github.com/osalpekar due to Broke caffe2 builds due. More info at [D46920675](https://www.internalfb.com/diff/D46920675) ([comment](https://github.com/pytorch/pytorch/pull/103725#issuecomment-1603129273))
 2023-06-22 18:30:31 +00:00
cyy
716b3b893d Use missing-prototypes in torch_cpu (#103725) 
This PR enables  Wmissing-prototypes in torch_cpu except some generated cpp files and the mps and metal backends.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/103725
Approved by: https://github.com/albanD
 2023-06-21 13:19:55 +00:00
Nikita Vedeneev
056d92e2a0 sparse.mm backward: performance improvements (#94991) 
`torch.sparse.mm` - faster and without syncs in "most" cases.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/94991
Approved by: https://github.com/Skylion007, https://github.com/pearu, https://github.com/cpuhrsch
 2023-06-12 20:57:29 +00:00
Nikita Karetnikov
2b3d955ffd [pt2] add meta and SymInt support for linalg_matrix_exp (#102945) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102945
Approved by: https://github.com/lezcano
 2023-06-09 22:45:16 +00:00
Nikita Karetnikov
4bda4a7e4d [pt2] add meta for lu_unpack (#102937) 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/102937
Approved by: https://github.com/lezcano
 2023-06-06 08:06:53 +00:00