Summary:
- Add support for linear and cubic interpolate in opset 11.
- Add support for 1d and 3d interpolate in nearest mode for opset 7 and 8.
- Add tests for all cases of interpolate in ORT tests (nearest/linear/cubic, 1d/2d/3d, upsample/downsample).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24805
Reviewed By: hl475
Differential Revision: D17330801
Pulled By: houseroad
fbshipit-source-id: 1bdefff9e72f5e70c51f4721e1d7347478b7505b
Summary:
Adds documentation for `nn.functional.bilinear`, as requested in https://github.com/pytorch/pytorch/issues/9886.
The format follows that of `nn.functional.linear`, and borrows from `nn.bilinear` in its description of `Tensor` shapes.
I am happy to add more extensive documentation (e.g. "Args," "Example(s)"). From what I gather, the format of comments is inconsistent across functions in `nn.functional.py` and between modules (e.g. `nn.functional` and `nn`). It's my first PR, so guidance for contributing documentation and other code would be greatly appreciated!
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24951
Differential Revision: D17091261
Pulled By: soumith
fbshipit-source-id: efe2ad764700dfd6f30eedc03de4e1cd0d10ac72
Summary:
Resolves: https://github.com/pytorch/pytorch/issues/20785
Addresses https://github.com/pytorch/pytorch/issues/24470 for `affine_grid`
Subsumes and closes: https://github.com/pytorch/pytorch/pull/24878 and likewise closes: https://github.com/pytorch/pytorch/issues/24821
Adds the `align_corners` option to `grid_sample` and `affine_grid`, paralleling the option that was added to `interpolate` in version 0.4.0.
In short, setting `align_corners` to `False` allows these functions to be resolution agnostic.
This ensures, for example, that a grid generated from a neural net trained to warp 1024x1024 images will also work to warp the same image upsampled/downsampled to other resolutions like 512x512 or 2048x2048 without producing scaling/stretching artifacts.
Refer to the documentation and https://github.com/pytorch/pytorch/issues/20785 for more details.
#### BC-Breaking Changes
- **Important**: BC-Breaking change because of new default for `align_corners`
The old functionality can still be achieved by setting `align_corners=True`, but the default is now set to `align_corners=False`, since this is the more correct setting, and since this matches the default setting of `interpolate`.
- **Should not cause BC issues**: BC-Breaking change for pathological use case
2D affine transforms on 1D coordinates and 3D affine transforms on 2D coordinates (that is, when one of the spatial dimensions has an empty span) are ill-defined, and not an intended use case of `affine_grid`. Whereas before, all grid point components along such dimension were set arbitrarily to `-1` (that is, before multiplying be the affine matrix), they are now all set instead to `0`, which is a much more consistent and defensible arbitrary choice. A warning is triggered for such cases.
#### Documentation
- Update `affine_grid` documentation to express that it does indeed support 3D affine transforms. This support was already there but not documented.
- Add documentation warnings for BC-breaking changes in `grid_sample` and `affine_grid` (see above).
#### Refactors
- `affine_grid` no longer dispatches to cuDNN under any circumstances.
The decision point for when the cuDNN `affine_grid_generator` is compatible with the native PyTorch version and when it fails is a headache to maintain (see [these conditions](5377478e94/torch/nn/_functions/vision.py (L7-L8))). The native PyTorch kernel is now used in all cases.
- The kernels for `grid_sample` are slightly refactored to make maintenance easier.
#### Tests
Two new tests are added in `test_nn.py`:
- `test_affine_grid_error_checking` for errors and warnings in `affine_grid`
- `test_affine_grid_3D` for testing `affine_grid`'s 3D functionality. The functionality existed prior to this, but wasn't tested.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/24929
Differential Revision: D16949064
Pulled By: ailzhang
fbshipit-source-id: b133ce0d47a2a5b3e2140b9d05fb05fca9140926
Summary:
Resolves: https://github.com/pytorch/pytorch/issues/20785
Adds the `align_corners` option to `grid_sample` and `affine_grid`, paralleling the option that was added to `interpolate` in version 0.4.0.
In short, setting `align_corners` to `False` allows these functions to be resolution agnostic.
This ensures, for example, that a grid generated from a neural net trained to warp 1024x1024 images will also work to warp the same image upsampled/downsampled to other resolutions like 512x512 or 2048x2048 without producing scaling/stretching artifacts.
Refer to the documentation and https://github.com/pytorch/pytorch/issues/20785 for more details.
**Important**: BC-Breaking Change because of new default
The old functionality can still be achieved by setting `align_corners=True`, but the default is now set to `align_corners=False`, since this is the more correct setting, and since this matches the default setting of `interpolate`.
The vectorized 2D cpu version of `grid_sampler` is refactored a bit. I don’t suspect that this refactor would affect the runtime much, since it is mostly done in inlined functions, but I may be wrong, and this has to be verified by profiling.
~The tests are not yet updated to reflect the new default. New tests should probably also be added to test both settings of `align_corners`.~ _Tests are now updated._
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23923
Differential Revision: D16887357
Pulled By: ailzhang
fbshipit-source-id: ea09aad7853ef16536e719a898db8ba31595daa5
Summary:
fix https://github.com/pytorch/pytorch/issues/21044
Bicubic interpolation can cause overshoot.
Opencv keeps results dtype aligned with input dtype:
- If input is uint8, the result is clamped [0, 255]
- If input is float, the result is unclamped.
In Pytorch case, we only accept float input, so we'll keep the result unclamped, and add some notes so that users can explicitly call `torch.clamp()` when necessary.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/23321
Differential Revision: D16464796
Pulled By: ailzhang
fbshipit-source-id: 177915e525d1f54c2209e277cf73e40699ed1acd
Summary:
* Deletes all weak script decorators / associated data structures / methods
* In order to keep supporting the standard library in script, this enables recursive script on any function defined in `torch.nn`
* Most changes in `torch/nn` are the result of `ag -Q "weak" torch/nn/ -l | xargs sed -i '/weak/d'`, only `rnn.py` needed manual editing to use the `ignore` and `export` to continue supporting the overloaded `forward` methods
* `Sequential`/`ModuleList` no longer need to be added to constants since they are compiled on demand
This should also fix https://github.com/pytorch/pytorch/issues/22212
Pull Request resolved: https://github.com/pytorch/pytorch/pull/22212
Differential Revision: D15988346
Pulled By: driazati
fbshipit-source-id: af223e3ad0580be895377312949997a70e988e4f
Summary:
The changes include:
1. Allow key/value to have different number of features with query. It supports the case when key and value have different feature dimensions.
2. Support three separate proj_weight, in addition to a single in_proj_weight. The proj_weight of key and value may have different dimension with that of query so three separate proj_weights are necessary. In case that key and value have same dimension as query, it is preferred to use a single large proj_weight for performance reason. However, it should be noted that using a single large weight or three separate weights is a size-dependent decision.
3. Give an option to use static k and v in the multihead_attn operator (see saved_k and saved_v). Those static key/value tensors can now be re-used when training the model.
4. Add more test cases to cover the arguments.
Note: current users should not be affected by the changes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21288
Differential Revision: D15738808
Pulled By: zhangguanheng66
fbshipit-source-id: 288b995787ad55fba374184b3d15b5c6fe9abb5c
Summary:
Currently multihead attention for half type is broken
```
File "/home/ngimel/pytorch/torch/nn/functional.py", line 3279, in multi_head_attention_forward
attn_output = torch.bmm(attn_output_weights, v)
RuntimeError: Expected object of scalar type Float but got scalar type Half for argument https://github.com/pytorch/pytorch/issues/2 'mat2'
```
because softmax converts half inputs into fp32 inputs. This is unnecessary - all the computations in softmax will be done in fp32 anyway, and the results need to be converted into fp16 for the subsequent batch matrix multiply, so nothing is gained by writing them out in fp32. This PR gets rid of type casting in softmax, so that half works.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/21658
Differential Revision: D15807487
Pulled By: zhangguanheng66
fbshipit-source-id: 4709ec71a36383d0d35a8f01021e12e22b94992d
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20665
Add gelu activation forward on CPU in pytorch
Compare to current python implemented version of gelu in BERT model like
def gelu(self, x):
x * 0.5 * (1.0 + torch.erf(x / self.sqrt_two))
The torch.nn.functional.gelu function can reduce the forward time from 333ms to 109ms (with MKL) / 112ms (without MKL) for input size = [64, 128, 56, 56] on a devvm.
Reviewed By: zheng-xq
Differential Revision: D15400974
fbshipit-source-id: f606b43d1dd64e3c42a12c4991411d47551a8121
Summary:
Remove the internal functions in multi_head_attention_forward. Those internal functions cause 10-15% performance regression and there is possibly a JIT issue.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20653
Differential Revision: D15398888
Pulled By: cpuhrsch
fbshipit-source-id: 0a3f053a4ade5009e73d3974fa6733c2bff9d929
Summary:
I believe the `True` and `False` in the doc are reversed :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20961
Differential Revision: D15510806
Pulled By: soumith
fbshipit-source-id: 62566bb595e187506b23dedc24892e48f35b1147
Summary:
Attention mask should be of shape `(L, S)` since it is added to `attn_output_weights`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20850
Differential Revision: D15495587
Pulled By: ezyang
fbshipit-source-id: 61d6801da5291df960daab273e874df28aedbf6e
Summary:
Remove weak_script. After recently splitting the forward() function in MultiheadAttention module, we notice a memory leak on GPU. Fix the problem by removing those "weak_script" decorator.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20563
Differential Revision: D15368262
Pulled By: zhangguanheng66
fbshipit-source-id: 475db93c9ee0dbaea8fb914c004e7d1e0d419bc2
Summary:
Moving functions from torch/nn/modules/activation.py to torch/nn/functional.py. For functions not implemented (_get_input_buffer and _set_input_buffer), a TODO is added.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20415
Differential Revision: D15318078
Pulled By: jamarshon
fbshipit-source-id: 5ca698e2913821442cf8609cc61ac8190496a3c6
Summary:
This PR add Poisson NLL loss to aten and substitute the python implementation with a call to the c++.
Fixes#19186.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19316
Differential Revision: D15012957
Pulled By: ezyang
fbshipit-source-id: 0a3f56e8307969c2f9cc321b5357a496c3d1784e
Summary:
fixes#20215
The confusing behavior was caused by typos in type annotation :(
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20306
Differential Revision: D15276216
Pulled By: ailzhang
fbshipit-source-id: 1b0c9635a72a05c9b537f80d85b117b5077fbec7
Summary:
in functional interfaces we do boolean dispatch, but all to max_pool\*d_with_indices. This change it to emit max_pool\*d op instead when it's not necessary to expose with_indices ops to different backends (for jit).
It also bind max_pool\*d to the torch namespace, which is the same behavior with avg_pool\*d
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19449
Differential Revision: D15016839
Pulled By: wanchaol
fbshipit-source-id: f77cd5f0bcd6d8534c1296d89b061023a8288a2c
Summary:
Fixes https://github.com/pytorch/pytorch/issues/10654
The issue is that in tracing `.size` returns an int tensor, and when an int tensor is multiplied by a scalar the int dominates and the scalar gets casted 0.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18875
Differential Revision: D14814441
Pulled By: eellison
fbshipit-source-id: a4e96a2698f2fcbf3ec4b2bb4c43a30250f30ad9
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18598
ghimport-source-id: c74597e5e7437e94a43c163cee0639b20d0d0c6a
Stack from [ghstack](https://github.com/ezyang/ghstack):
* **#18598 Turn on F401: Unused import warning.**
This was requested by someone at Facebook; this lint is turned
on for Facebook by default. "Sure, why not."
I had to noqa a number of imports in __init__. Hypothetically
we're supposed to use __all__ in this case, but I was too lazy
to fix it. Left for future work.
Be careful! flake8-2 and flake8-3 behave differently with
respect to import resolution for # type: comments. flake8-3 will
report an import unused; flake8-2 will not. For now, I just
noqa'd all these sites.
All the changes were done by hand.
Signed-off-by: Edward Z. Yang <ezyang@fb.com>
Differential Revision: D14687478
fbshipit-source-id: 30d532381e914091aadfa0d2a5a89404819663e3
Summary:
Addind the same warning message already present in the mse_loss function to the L1 losses when input and target sizes are different.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18565
Differential Revision: D14671415
Pulled By: soumith
fbshipit-source-id: 01f5e1fb1ea119dbb2aecf1d94d0cb462f284982
Summary:
To address the issue of broadcasting giving the wrong result in `nn.MSELoss()` as mentioned here https://github.com/pytorch/pytorch/issues/16045 . In particular, the issue often arises when computing the loss between tensors with shapes (n, 1) and (n,)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18349
Differential Revision: D14594176
Pulled By: soumith
fbshipit-source-id: f23ae68a4bf42f3554ad7678a314ba2c7532a6db
Summary:
In the loss doc description, replace the deprecated 'reduct' and 'size_average' parameters with the 'reduction' parameter.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17300
Differential Revision: D14195789
Pulled By: soumith
fbshipit-source-id: 625e650ec20f13b2d22153a4a535656cf9c8f0eb
Summary:
Eventually we should remove these when we're certain that all our ops
handle memory overlaps correctly.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17576
Differential Revision: D14349990
Pulled By: zou3519
fbshipit-source-id: c3a09f6113b9b1bf93e7f13c0b426c45b2cdf21f
Summary:
one_hot docs is missing [here](https://pytorch.org/docs/master/nn.html#one-hot).
I dug around and could not find a way to get this working properly.
Differential Revision: D14104414
Pulled By: zou3519
fbshipit-source-id: 3f45c8a0878409d218da167f13b253772f5cc963
Summary:
- Moved a few functions from `autograd` namespace to `aten` namespace to be visible from JIT nativeResolver.
- Added a hack to loop up keyword only argument. Will add proper support for kw only later
- Simulate function overload in aten using `_<number>` as function name suffix.
- Even `forward` returns multiple outputs like in `kthvalue`, there's at most one requires grad that we currently support.
- Removed the `TensorList` related ops here since partial `TensorList` support is prone to bugs. Our symbolic diff for `cat` was never tested with autodiff, and it seems broken. Need to find another proper way to support these ops(either by properly supporting `TensorList` or sth like `prim::ConstantChunk` and leave them for next PR.
Ops supported in this PR:
```
erf
expand_as
index
kthvalue
mean
permute
pow
rsub
select
sqrt
squeeze
t
to
topk
transpose
view
var
embedding
logsumexp
// grad is None
_dim_arange
contiguous
nonzero
ones_like
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16689
Differential Revision: D14020806
Pulled By: ailzhang
fbshipit-source-id: a5e2c144a7be5a0d39d7ac5f93cb402ec12503a5
Summary:
The use case for making this PR is the following bug :
(with F = torch.nn.functional)
`F.max_pool2d.__module__` is `torch._jit_internal`
`F.max_pool2d.__name__` is `fn`
With this PR you get:
`F.max_pool2d.__module__` is `torch.nn.functional`
`F.max_pool2d.__name__` is `max_pool2d`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16922
Differential Revision: D14020053
Pulled By: driazati
fbshipit-source-id: c109c1f04640f3b2b69bc4790b16fef7714025dd
Summary:
Here is a stab at implementing an option to zero out infinite losses (and NaN gradients).
It might be nicer to move the zeroing to the respective kernels.
The default is currently `False` to mimic the old behaviour, but I'd be half inclined to set the default to `True`, because the behaviour wasn't consistent between CuDNN and Native anyways and the NaN gradients aren't terribly useful.
This topic seems to come up regularly, e.g. in #14335
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16199
Differential Revision: D14020462
Pulled By: ezyang
fbshipit-source-id: 5ba8936c66ec6e61530aaf01175dc49f389ae428
Summary:
This PR is a follow up of #15460, it did the following things:
* remove the undefined tensor semantic in jit script/tracing mode
* change ATen/JIT schema for at::index and other index related ops with `Tensor?[]` to align with what at::index is really doing and to adopt `optional[tensor]` in JIT
* change python_print to correctly print the exported script
* register both TensorList and ListOfOptionalTensor in JIT ATen ops to support both
* Backward compatibility for `torch.jit.annotate(Tensor, None)`
List of follow ups:
* remove the undefined tensor semantic in jit autograd, autodiff and grad_of
* remove prim::Undefined fully
For easy reviews, please turn on `hide white space changes` in diff settings.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16379
Differential Revision: D13855677
Pulled By: wanchaol
fbshipit-source-id: 0e21c14d7de250c62731227c81bfbfb7b7da20ab
Summary:
Changelog:
- Modify concantenation of [1] to a tuple by using cases for list and non-list types.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16489
Differential Revision: D13875838
Pulled By: soumith
fbshipit-source-id: fade65cc47385986b773b9bde9b4601ab93fe1cf
Summary:
Remove calls to torch.jit._unwrap_optional that are no longer needed.
The remaining instances would require control flow logic for exceptions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16245
Differential Revision: D13804292
Pulled By: eellison
fbshipit-source-id: 08c5cbe4b956519be2333de5cf4e202488aff626
Summary:
Fixes#12643, amends to #3341.
- Allow multidimensional input ~~(but apply softmax over `dim=-1`)~~ with `dim` argument
- Cleaner: Less lines of code
- Faster (1.32x speedup vs original, 2x speedup vs using `torch.Distributions`)
- Small fixes in docstring
- Remove some references in docstring. Was the linked (excellent) ipynb the first to do the straight-through trick? Instead, I propose changing to reference to the two papers most known for it.
- Add deprecationwarning for `eps`. It's not needed anymore.
- Initial commit keeps some code alternatives commented to exploit CI
- As of discussion when `gumbel_softmax` was added (#3341), this was merged into `torch.nn.functional` before all the work with `Distributions` and `Pyro`, and there will probably be multiple other best practices for this in the future.
I've tested building using the `Distributions`-api, but it was too slow, see below.
I therefore propose not using `Distributions` to keep it fast and simple, but adding a comment in docstring that `gumbel_softmax` may be deprecated in the future.
```
dist = torch.distributions.RelaxedOneHotCategorical(temperature=tau, logits=logits, validate_args=False)
y_soft = dist.rsample()
```
Pros:
* Built using tricks like `logsumexp` etc
* Explicitly uses `torch.distributions.utils._finfo` to avoid overflow (old implementation had an `eps` flag)
* Maintained for this exact purpose.
Cons:
* Very slow. Construction of distribution adds overhead see timings below. May be solved in future with speedups of `TransformedDistribution` and `Distribution`.
* Assumes which `dim` to apply softmax over.
```
y_soft = logits.new(logits.shape)
y_soft = (logits - y_soft.exponential_().log()) / tau # Gumbel noise
y_soft = y_soft.softmax(dim) # Gumbel softmax noise
```
Pros:
* Faster
```
import time
start = time.time()
num_draws = 1000000
logits = torch.randn(1,3)
for draw in range(num_draws):
y_draw = gumbel_softmax(logits, hard=True)
counts = counts + y_draw
print(end - start)
>> 12.995795965194702
>> 7.658372640609741
>> 20.3382670879364
````
Decide on which path to chose. I'll commit in changes to the unit tests in a while to show that it passes both old tests and new tests. I'll also remove the commented code about `RelaxedOneHotCategorical`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13339
Differential Revision: D13092434
Pulled By: ezyang
fbshipit-source-id: 4c21788df336f4e9c2ac289022e395b261227b4b
Summary:
1) Reverts https://github.com/pytorch/pytorch/pull/12302 which added support for batched pdist. Except I kept the (non-batched) test improvements that came with that PR, because they are nice to have. Motivation: https://github.com/pytorch/pytorch/issues/15511
2) For the non-batched pdist, improved the existing kernel by forcing fp64 math and properly checking cuda launch errors
3) Added a 'large tensor' test that at least on my machine, fails on the batch pdist implementation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15901
Reviewed By: ezyang
Differential Revision: D13616730
Pulled By: gchanan
fbshipit-source-id: 620d3f9b9acd492dc131bad9d2ff618d69fc2954
Summary:
1. Port the FractionalMaxPool3d implementation from THNN/THCUNN to ATen.
2. Expose this function to Python module nn.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15575
Differential Revision: D13612848
Pulled By: chandlerzuo
fbshipit-source-id: 5f474b39005efa7788e984e8a805456dcdc43f6c
Summary:
Previously we were only constant propping prim::Constants, but we should be constant propping prim::None as well.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15979
Differential Revision: D13664692
Pulled By: eellison
fbshipit-source-id: 01839403576c21fc030c427e49275b8e1210fa8f
Summary:
- Fixed a few typos and grammar errors.
- Changed the sentences a bit.
- Changed the format of the tuples to be consistent with padding notations in the other places. For example, `ReflectionPad2d`'s dostring contains :math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`.
I also made sure that the generated html doesn't break.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15984
Differential Revision: D13649939
Pulled By: soumith
fbshipit-source-id: 0abfa22a7bf1cbc6546ac4859652ce8741d41232
Summary:
This updates pdist to work for batched inputs, and updates the
documentation to reflect issues raised.
closes#9406
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12302
Reviewed By: ezyang
Differential Revision: D13528485
Pulled By: erikbrinkman
fbshipit-source-id: 63d93a6e1cc95b483fb58e9ff021758b341cd4de
Summary:
Addresses #918, interpolation results should be similar to tf
* Adds bicubic interpolation operator to `nn.functional.interpolate`
* Corresponding test in `test_nn.py`
The operator is added in legacy `TH` to be aligned with the other upsampling operators; they can be refactored/moved to ATen all at once when #10482 is resolved
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9849
Differential Revision: D9007525
Pulled By: driazati
fbshipit-source-id: 93ef49a34ce4e5ffd4bda94cd9a6ddc939f0a4cc
Summary:
This PR adds `None` buffers as parameters (similarly to #14715). It also cleans up a bunch of the `test_jit.py` tests that should be covered by `common_nn.py` and brings in `criterion_tests` to test loss functions.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14778
Differential Revision: D13330849
Pulled By: driazati
fbshipit-source-id: 924cc4cf94e0dcd11e811a55222fd2ebc42a9e76
Summary:
Fixes#6622 .
We used to average over all elements for kl divergence, which is not aligned with its math definition.
This PR corrects the default reduction behavior of KL divergence that it now naverages over batch dimension.
- In KL, default behavior `reduction=mean` averages over batch dimension. While for most other loss functions, `reduction=mean` averages over all elements.
- We used to support scalar tensor as well. For BC purpose, we still support it, no reduction is performed on scalar tensor.
- Added a new reduction mode called `batchmean` which has the correct behavior for KL. Add a warning to make `batchmean` as default for KL instead of `mean` in next major release.
- [deprecated]I chose to not add a new reduction option, since "mean over batch dimension" is kinda special, and it only makes sense in few cases like KL. We don't want to explain why there's a option "batchmean" but it's not applicable for all other functions. I'm open to discussion on this one, as I cannot think of a perfect solution for this.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14457
Differential Revision: D13236016
Pulled By: ailzhang
fbshipit-source-id: 905cc7b3bfc35a11d7cf098b1ebc382170a087a7
Summary:
Remove no_grad_embedding_renorm_ from aten. Setting the derivatives of the inputs to false has different semantics from calling with no_grad(), because it will not error if an input is modified and then has it's grad accessed.
Instead, make a custom op, and use NoGradGuard.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14639
Differential Revision: D13285604
Pulled By: eellison
fbshipit-source-id: c7d343fe8f22e369669e92799f167674f124ffe7
Summary:
This PR adds a polyfill for `typing.List` for Python versions that don't
support `typing` as a builtin. It also moves the type defintions from
`annotations.py` so that they can be used in `torch.nn`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14482
Differential Revision: D13237570
Pulled By: driazati
fbshipit-source-id: 6575b7025c2d98198aee3b170f9c4323ad5314bd
Summary:
To convert `max_unpool` functions to weak script, this PR adds support
for `T` as default arguments for `BroadcastingListN[T]`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14361
Differential Revision: D13192231
Pulled By: driazati
fbshipit-source-id: a25b75a0e88ba3dfa22d6a83775e9778d735e249
Summary:
This PR adds weak modules for all activation modules and uses `test_nn` module tests to test weak modules that have been annotated with `weak_module` and therefore are in `torch._jit_internal._weak_types`
Also depends on #14379
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14238
Differential Revision: D13252887
Pulled By: driazati
fbshipit-source-id: e9638cf74089884a32b8f0f38396cf432c02c988
Summary:
Resubmitting PR #14415
The tests added for Embedding + EmbeddingBag had random numbers as input, which affected the random number generator & caused the flakey test to break.
Everything but the last two commits have already been accepted
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14509
Differential Revision: D13247917
Pulled By: eellison
fbshipit-source-id: ea6963c47f666c07687787e2fa82020cddc6aa15
Summary:
Add support for Embedding and EmbeddingBag in script. Both functions require with torch.no_grad(), which we don't have any plans to support in the near future. To work around this, I added a embedding_renorm function without derivatives.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14415
Reviewed By: wanchaol
Differential Revision: D13219647
Pulled By: eellison
fbshipit-source-id: c90706aa6fbd48686eb10f3efdb65844be7b8717
Summary:
This PR adds weak modules for all activation modules and uses `test_nn` module tests to test weak modules that have been annotated with `weak_module` and therefore are in `torch._jit_internal._weak_types`
Also depends on #14379
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14238
Differential Revision: D13192230
Pulled By: driazati
fbshipit-source-id: 36488960b6c91448b38c0fa65422539a93af8c5e
Summary:
This PR allows to overload functions based on the value of a parameter (so long as it is a constant). See max_pool1d for an example usage.
This is the first step in enabling the use of max_pool functions for the standard library that can return `Tensor` or `Tuple[Tensor, Tensor]` based on the `return_indices` flag. This will give the JIT identical results to the Python versions of the functions.
Fixes#14081
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14425
Differential Revision: D13222104
Pulled By: driazati
fbshipit-source-id: 8cb676b8b13ebcec3262234698edf4a7d7dcbbe1
Summary:
Port AffineGrid to C++, because script does not support compiling Function classes.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14392
Differential Revision: D13219698
Pulled By: eellison
fbshipit-source-id: 3ddad8a84c72010b5a6c6f7f9712be614202faa6
Summary:
This PR allows to overload functions based on the value of a parameter (so long as it is a constant). See `max_pool1d` for an example usage.
This is the first step in enabling the use of `max_pool` functions for the standard library that can return `Tensor` or `Tuple[Tensor, Tensor]` based on the `return_indices` flag. This will give the JIT identical results to the Python versions of the functions.
Depends on #14232 for `Optional[BroadcastingList[T]]`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14081
Differential Revision: D13192228
Pulled By: driazati
fbshipit-source-id: fce33c400c1fd06e59747d98507c5fdcd8d4c113
Summary:
1. Support `Optional[BroadcastingList1[int]]` like type annotation to accept a int or a list[int]
2. Convert gumbel_softmax, lp pooling weak functions and modules
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14232
Differential Revision: D13164506
Pulled By: wanchaol
fbshipit-source-id: 6c2a2b9a0613bfe907dbb5934122656ce2b05700
Summary:
This PR inserts `prim::None` constants for undefined tensors. This comes in the standard library if an `Optional[Tensor]` is statically determined to be `None`:
```python
torch.jit.script
def fn(x=None):
# type: (Optional[Tensor]) -> Tensor
return torch.jit._unwrap_optional(x)
torch.jit.script
def fn2():
# type: () -> Tensor
return fn()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14120
Differential Revision: D13124625
Pulled By: driazati
fbshipit-source-id: 9eaa82e478c49c503f68ed89d8c770e8273ea569
Summary:
This PR did three things:
1. It export the BatchNorm functional and module, and rewrite some of the components to stay align with the current supported JIT features
2. In the process of export, add necessary compiler support for in_place op aug assign
4. change the test_jit behavior in add_module_test to utilize a single rng state during module initialization
Pull Request resolved: https://github.com/pytorch/pytorch/pull/14016
Differential Revision: D13112064
Pulled By: wanchaol
fbshipit-source-id: 31e3aee5fbb509673c781e7dbb6d8884cfa55d91
Summary:
This PR did two thing:
1. it fix the optional import/export to include any type including tensor types (previously we only support base types), this is essential to unblock optional tensor type annotation in our test logic
2. it tries to export mult_margin_loss functional to serve as a example of optional undefined tensor use case.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13877
Differential Revision: D13076090
Pulled By: wanchaol
fbshipit-source-id: c9597295efc8cf4b6462f99a93709aae8dcc0df8
Summary:
I'm now traveling and don't have access to a good computer to compile test by myself. Will see the outcome of CI.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/12199
Differential Revision: D13062326
Pulled By: nairbv
fbshipit-source-id: 85873525caa94906ccaf2c739eb4cd55a72a4ffd
Summary:
Convert some more functions to match up with features added. Some
conversions were unsuccessful but the type line was left in for later.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13707
Differential Revision: D13030210
Pulled By: driazati
fbshipit-source-id: 02d5712779b83b7f18d0d55539e336321335e0cc
Summary:
* Adds `OptionalType` support for import/export
* Optionals get exported along with their contained type, i.e. 'Optional[int]'
* Allows concrete types and `None` to be passed to an op that takes an optional
* Converts `softmax`
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13647
Differential Revision: D12954672
Pulled By: driazati
fbshipit-source-id: 159e9bfb7f3e398bec3912d414c393098cc7455a
Summary:
This PR is a part of task to unblock standard library export.
* we treat None differently from Tensor and other types, when passing None as Tensor, it's an undefined tensor rather than the None IValue.
* Refine the type system so that we have correct tensor types hierarchy (Dynamic/Tensor/CompleteTensor), Dynamic should be at the top of the inheritance hierarchy.
* It also tries to export bilinear as an example of undefined tensor(None) input.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13650
Differential Revision: D12967026
Pulled By: wanchaol
fbshipit-source-id: 6aedccc7ce2a12fadd13d9e620c03e1260103a5a
Summary:
To support `_Reduction` in the jit this PR moves it out to a new file so that it goes through the paths for python modules in the script compiler and converts `F.ctc_loss` to weak script
Depends on #13484 for saving rng state
Pull Request resolved: https://github.com/pytorch/pytorch/pull/13401
Differential Revision: D12868501
Pulled By: driazati
fbshipit-source-id: 23cec0fb135744578c73e31ac825e238db495d27
