When exporting dropout with cpu tensor, we get following graph module
```
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[512, 10]):
empty_memory_format: f32[512, 10] = torch.ops.aten.empty.memory_format([512, 10], dtype = torch.float32, layout = torch.strided, device = device(type='cpu'), pin_memory = False, memory_format = torch.contiguous_format)
bernoulli_p: f32[512, 10] = torch.ops.aten.bernoulli.p(empty_memory_format, 0.9); empty_memory_format = None
div_scalar: f32[512, 10] = torch.ops.aten.div.Scalar(bernoulli_p, 0.9); bernoulli_p = None
mul_tensor: f32[512, 10] = torch.ops.aten.mul.Tensor(arg0_1, div_scalar); arg0_1 = div_scalar = None
return (mul_tensor,)
```
In addition, if we export with eval() mode, we will have an empty graph.
However, when exporting with cuda tensor, we got
```
class GraphModule(torch.nn.Module):
def forward(self, arg0_1: f32[512, 10]):
native_dropout_default = torch.ops.aten.native_dropout.default(arg0_1, 0.1, True); arg0_1 = None
getitem: f32[512, 10] = native_dropout_default[0]; native_dropout_default = None
return (getitem,)
```
and exporting under eval() mode will still have a dropout node in graph.
This PR make exporting with CPU tensor also produce aten.native_dropout.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/106274
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
This updates ruff to 0.285 which is faster, better, and have fixes a bunch of false negatives with regards to fstrings.
I also enabled RUF017 which looks for accidental quadratic list summation. Luckily, seems like there are no instances of it in our codebase, so enabling it so that it stays like that. :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/107519
Approved by: https://github.com/ezyang
- Added ops that were missing under `__all__`.
- Some misc changes to helper functions to make them private.
- Set correct `fn.__module__` for `fn` created by `_make_alias`, when called in another module.
All modification largely references results from a hacked version of `test_public_bindings::test_correct_module_names`.
By default `torch._refs` is not included in the test because it is technically a private package.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103712
Approved by: https://github.com/lezcano
Use Prims to implement group_norm, group_norm_backward and mean_var
Use `torch._ops.ops` instead of `torch.ops` in numerous subpackages in
order to be able to make them importable from `torch/backend/mps/__init__.py` as this alias is defined in
15af4b1cee/torch/__init__.py (L1095)
is executed last during init process.
Add `__all__` to `torch/backends/mps/__init__.py` as well as alias all imports as private
Add `TestNNMPS.test_group_norm_backward` that validates no NaNs are generated during the backward pass
Fixes https://github.com/pytorch/pytorch/issues/88331
Pull Request resolved: https://github.com/pytorch/pytorch/pull/91190
Approved by: https://github.com/albanD
We add most in-place references in a generic way. We also implement a
wrapper to implement the annoying interface that `nn.functional`
nonlinearities have.
We fix along the way a couple decompositions for some non-linearities by
extending the arguments that the references have.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88117
Approved by: https://github.com/mruberry
The `__name__` field of some binary reference functions was wrong. We
fix this to be consistent with unary reference functions. In the future,
we should probably make the binary reference wrapper return a wrapper
itself to avoid all those calls to `partial`.
This change helps performing some homogeneous treatment of functions by
their name.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/88115
Approved by: https://github.com/mruberry
This PR adds refs for the following ops:
- `torch.triu`
- `torch.tril`
- `torch.triu_indices`
- `torch.tril_indices`
- `torch.nn.functional.pairwise_distance`
- `torch.nn.functional.pdist`
It adds OpInfos for
- `torch.triu_indices`
- `torch.tril_indices`
Note that these were already tested in `test/test_tensor_creation_ops.py`
but for the ref tests we need the OpInfos.
Finally, it improves documentation for PairwiseDistance and adds
a missing import to `torch/testing/_internal/opinfo/core.py`.
This started with an attempt to just add the `nn.functional` refs above,
but it turned out that `pdist` was easiest to implement using `triu_indices`
so I added that one and the related functions as well.
~~In the end, I changed the `pdist` implementation to not use `triu_indices`
but kept the other refs.~~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82819
Approved by: https://github.com/ngimel
